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The Freidel-Crafts sulfination reaction of acetanilide with sulfur dioxide was catalyzed by several AlCl3-based liquid coordination complexes (LCCs), and the results were compared with that catalyzed by traditional ILs. Influences of different catalysts, ligand/AlCl3 molar ratios, and reaction conditions on their catalytic performance were investigated. The optimal result was obtained using acetamide-AlCl3-based LCC (molar ratio of acetamide to AlCl3 was 0.65, marked as 0.65 A A/AlCl3) as the catalyst, giving nearly 100% conversion of acetanilide within 60 min and 94.07% of selectivity to 4-acetamidobenzenesulfinic acid. AA/AlBr3 was synthesized and also showed catalytic activity for the sulfination reaction. Then a plausible reaction mechanism was proposed by investigation through the raman spectra.

Dendrites form on the surface of metal electrodes as a battery undergoes repeated charging and discharging. The formation of dendrites not only affects the electrochemical performances of a battery but may also cause internal short-circuiting and thermal runaway events. Hence, there is a clear need to understand the formation mechanisms and growth kinetics of dendrites. Currently available technologies such as (in-situ) X-ray computed tomography and scanning electron microscopy can be used in a laboratory environment to study dendrite formation. However, they are often prohibitively expensive or unsuitable for long-term, large-scale, and in-operando monitoring. In this study we present an ultrasound-based technique which is cheap, with potential to scale up, and suitable in both laboratory and industrial environments. Different from conventional ultrasonic approaches which excite ultrasonic waves that propagating through layers of cathode, anode and electrolyte, the method presented in this study utilises miniature transducers which excite fundamental shear-horizontal (SH0*) guided ultrasonic waves that propagate along the cathode and anode. Hence, the presented method can individually monitor the condition of multiple electrodes which may be undergoing different ion-insertion mechanisms. A proof-of-concept study was performed on a symmetric aqueous zinc ion cell to explore the sensitivity of SH0* mode guided ultrasonic waves to zinc metal plating/stripping and dendrite formation at the electrode-electrolyte interface. We observed that the attenuation and velocity of waves show cyclical variations when the cell was in a chronopotentiometric cycling test. Variations of wave attenuation and velocity on the opposing working and counter electrode are observed to be in the opposite senses and the increasing/decreasing trends depend on the states (i.e., charging/discharging) of the cell. The cyclical variations of the ultrasound properties are only partially reversible and a monotonic drift can be observed which increases with increasing number of cycles. Coupling the ultrasonic measurements with images acquired by an optical microscope, clear correlations can be observed between changes in ultrasonic wave properties and zinc dendrite thickness. The partially reversible cyclical variations can be attributed to zinc plating/stripping that occur at the electrode-electrolyte interface. The irreversible, monotonic drifting can be attributed to the growth of zinc dendrites on the electrode surface. Hence, these findings demonstrate that the SH0* mode guided ultrasonic waves can offer valuable insights on physical changes at the electrode-electrolyte interface. It is therefore possible to quantitatively model and predict dendrite growth based on ultrasonic measurements. In conclusion, we believe the presented method can inspire more quantitative research on dendrite formation and in-operando monitoring of battery health. Figure 1

El Niño and Southern Oscillation (ENSO) is closely related to a series of regional extreme climates, so robust long-term forecasting is of great significance for reducing economic losses caused by natural disasters. Here, we regard ENSO prediction as an unsupervised spatiotemporal prediction problem, and design a deep learning model called Dense Convolution-Long Short-Term Memory (DC-LSTM). For a more sufficient training model, we will also add historical simulation data to the training set. The experimental results show that DC-LSTM is more suitable for the prediction of a large region and a single factor. During the 1994–2010 verification period, the all-season correlation skill of the Nino3.4 index of the DC-LSTM is higher than that of the current dynamic model and regression neural network, and it can provide effective forecasts for lead times of up to 20 months. Therefore, DC-LSTM can be used as a powerful tool for predicting ENSO events.

Accurate classification of the children's epilepsy syndrome is vital to the diagnosis and treatment of epilepsy. But existing literature mainly focuses on seizure detection and few attention has been paid to the children's epilepsy syndrome classification. In this paper, we present a study on the classification of two most common epilepsy syndromes: the benign childhood epilepsy with centro-temporal spikes (BECT) and the infantile spasms (also known as the WEST syndrome), recorded from the Children's Hospital, Zhejiang University School of Medicine (CHZU). A novel feature fusion model based on the deep transfer learning and the conventional time-frequency representation of the scalp electroencephalogram (EEG) is developed for the epilepsy syndrome characterization. A fully connected network is constructed for the feature learning and syndrome classification. Experiments on the CHZU database show that the proposed algorithm can offer an average of 92.35% classification accuracy on the BECT and WEST syndromes and their corresponding normal cases.

As for the anti-pendulum positioning problem of distributing quality double pendulum bridge crane system, this paper designs a New Time-Varying Sliding Mode Control (NTVSMC). To be specific, first of all, the dynamic equation is established based on the distributed mass model of the double pendulum bridge crane. Then, a time-varying parameter is designed to realize the dynamic adjustment of sliding mode surface and enhance the adjustment ability of the sliding mode controller. Moreover, the asymptotic stability of the system is proved by the Lyapunov method. Finally, numerical simulation is used to verify that the proposed NTVSMC has good control performance and strong robustness against external interference. Simulation experiments show that comparing with conventional CSMC, the control method proposed in this paper can effectively reduce the driving force of the trolley, ensure the rapid and accurate positioning of the trolley and effectively restrain the load swing angle within 0.6 degrees or even less, with almost no residual swing angle.

Recently, the signal hidden layer feedforward network (SLFN) based extreme learning machine (ELM) has been extended to a hierarchical learning framework (HELM). Although the HELM shows better generalization performance with lower computational complexity than many deep neural networks (DNNs), it is found that as the layer increases, the input distribution of each layer may move to the saturated regime of the non-linear activation function, which affects the generalization performance. However, few attentions have been paid to the data distribution normalization to address this issue. Thus, in this paper, an affine transformation (AT) inputs based activation function layer is introduced to normalize the data distribution and a novel AT based HELM (AT-HELM) is developed. The proposed AT-HELM can adapt the activation function inputs to the distribution of each layer and obtains better generalization performance. Experiments on 29 benchmark datasets are carried out to demonstrate the superiority of AT-HELM.

Ceramic oxygen permeable membranes (OPMs) are widely considered the “next generation” technology for pure oxygen separation and catalytic membrane reactors. However, the main issue of OPM is high operating temperature (above 800 °C) and resulting implications for materials, cost, as well as operating complexities (e.g., sealing, creep deformation, and thermal shock during thermal cycling). A new design for coupling OPM La0.6Sr0.4Co0.2Fe0.8O3-δ with a dielectric barrier discharge plasma (a type of atmospheric pressure non-thermal plasma) is proposed for highly efficient low-temperature (600 °C) oxygen permeation. This study demonstrates that the plasma can activate the surface exchange reactions. Applying 15 W air-plasma can effectively reduce the apparent activation energy of the permeation process from 136.6 to 43.1 kJ mol−1. The oxygen flux is increased by a factor of nearly 30 at 600 °C with a plasma power of 15 W. The newly developed plasma-membrane micro-reactor design enables further performance enhancement at lower operating temperatures and integration with solid oxide fuel cells, catalytic membrane reactors, and oxygen permeable membranes.

Earth surface vibration signal processing becomes attractive recently due to its significance in source detection and localization, which can be adopted in a number of real world applications, such as footstep detection, underground pipeline network surveillance, etc. In this paper, we investigate the distance estimation problem for earth surface periodic vibration signal localization. The signal attenuation principle between the propagation distance and the signal frequency is exploited and a novel frequency band energy percentage (FBEP) feature is developed to characterize the energy distribution property within different frequency bands of different propagation distances. To obtain the fundamental frequency of periodic vibrations, the cepstrum approach is employed. An enhanced computationally efficient k nearest neighborhood (EH-kNN) algorithm is developed to perform the distance estimation. Experiments on real periodic vibration signals generated by an electric hammer under different collecting distances and transmission medias are conducted to show the superiority of the proposed distance estimation method in this paper.

The imbalanced data classification attracts increasing attention in the past years due to the continuous expansion of data available in many areas, such as biomedical engineering, surveillance, and computer vision. Learning from imbalanced data is challenging as most standard algorithms fail to properly represent the inherent complex characteristics of the data distribution. As an emerging technology, the extreme learning machine (ELM) and its variants, including the weighted ELM (WELM) and the boosting weighted ELM (BWELM), have been recently developed for the classification of imbalanced data. However, the WELM suffers the following deficiencies: (i) the sample weight matrix is manually chosen and fixed during the learning phase; (ii) the representation capability, namely the capability to extract features or useful information from the original data, is insufficiently explored due to its shallow structure. The BWELM employs the AdaBoost algorithm to optimize the sample weights. But the representation capability is still restricted by the shallow structure. To alleviate these deficiencies, we propose a novel deep weighted ELM (DWELM) algorithm for imbalanced data classification in this paper. An enhanced stacked multilayer deep representation network trained with the ELM (EH-DrELM) is first proposed to improve the representation capability, and a fast AdaBoost algorithm for imbalanced multiclass data (AdaBoost-ID) is developed to optimize the sample weights. Then, the novel DWELM for the imbalance learning is obtained by combining the above two algorithms. Experimental results on nine imbalanced binary-class datasets, nine imbalanced multiclass datasets, and five large benchmark datasets (three for multiclass and two for binary-class) show that the proposed DWELM achieves a better performance than the WELM and BWELM, as well as several state-of-the-art multilayer network-based learning algorithms.

A safe and efficient protocol for the synthesis of aryl sulfinates from arenes and 1,4-diazabicyclo[2,2,2]octane bis(sulfur dioxide) (DABSO) under mild conditions is described. Through in situ infrared spectroscopic studies, a plausible mechanism for the sulfination reaction is proposed as well.

Earth surface vibrations generated by passing vehicles, excavation equipment, footsteps, etc., attract increasing attentions in the research community due to their wide applications. In this paper, we investigate the periodic vibration source localization problem, which has recently shown significance in excavation device detection and localization for urban underground pipeline network protection. An intelligent propagation distance estimation algorithm based on a novel fundamental frequency energy distribution (FBED) feature is developed for periodic vibration signal localization. Contributions of the paper lie in three aspects: 1) a novel frequency band energy distribution (FBED) feature is developed to characterize the property of vibrations at different propagation distances; 2) an intelligent propagation distance estimation model built on the FBED feature with machine learning algorithms is proposed, where for comparisons, the support vector machine (SVM) for regression and regularized extreme learning machine (RELM) are used; 3) a localization algorithm based on the distance-of-arrival (DisOA) estimation using three piezoelectric transducer sensors is given for source position estimation. To testify the effectiveness of the proposed algorithms, case studies on real collected periodic vibration signals generated by two electric hammers with different fundamental frequencies are presented in the paper. The transmission medium is the cement road and experiments on vibration signals recorded at different propagation distances are conducted.

To improve the control performance of anti-swinging and the orientation of two-dimensional bridge crane, a novel dynamic sliding mode variable structure control algorithm is presented here. The proposed controller can ensure global stability through improved design of sliding surface for the drive control force, and obtain the continuous driving control force in time domain, by using the Sigmoid function for switching function to suppress chattering. Experimental results demonstrated that it is effective on chattering suppression, swing reduction, and robust with various load qualities and friction resistances.

Fast and accurate tracking is ultimate important to dynamic overhead cranes control. This work presents a simple yet effective fast dynamic overhead cranes tracking method, enabling it to be applied to accurate and real time controlled. Firstly, a sparse measurement matrix is utilized to acquire the features. Secondly, compressed samples are utilized in constructing sparse measurement matrix. Finally, tracking of dynamic overhead cranes is represented as a binary classification with Bayesian classifier. Experimental results on VOT2013 benchmark and self-built Cranes40 dataset show that the proposed sparse representation tracking algorithm could achieve satisfying results on tracking overhead cranes.

The cation segregation in a perovskite-type membrane deleteriously affects the membrane performance and durability. Recovery of segregation is a great challenge because of the complexity of segregation products and severe limitations from the stability of perovskite oxide. This work presents a novel approach for reversing the surface cation segregation by hydrogen-induced annealing which further shows the ability of healing defects and cracks in the oxide bulk. Ba0.3Sr0.7Fe0.9Mo0.1O3-δ (BSFM) four-channel hollow fiber membranes were prepared by one-step thermal processing. A low hydrogen concentration (5 vol %) and a mild temperature (900 °C) were used to anneal the as-prepared BSFM hollow fiber membrane and served to trigger outright reverse segregation of massive complex oxide precipitations at grain boundaries and a self-healing of cracks formed during the sintering stage. The reverse segregation significantly alters the composition and structure which directly affects the performance of BSFM membrane, such as mechanical strength, electrical conductivity, oxygen permeability, and stability. A maximum breaking load of 31.6 N is reached for the hydrogen-induced membrane which is approximately 6 times of the as-prepared BSFM membrane. Hydrogen-induced membranes, having high oxygen permeation flux and low activation energy, can operate stably in partial oxidation of methane catalytic membrane reactor for more than 120 h. Our results demonstrate the feasibility of hydrogen-induced reverse segregation and crack self-healing in the perovskite-type dense membrane, suggesting new directions to design the high-performance materials based on perovskite oxides for application in catalysis, electrocatalysis, and separations in the future.

Fear of heights (FoH) analysis and its association to physiological signals can better help understand people’s emotion and quantify human’s behavior, which have been found important in many applications, such as disease analysis, affective computing, etc. Existing studies are mainly on how to alleviate FoH while little literature on FoH analysis has been reported in the past. In this paper, we present the studies of correlation on FoH to multi-modal physiological signals in the virtual reality (VR). To stimulate the FoH to participants, 4 types of VR scenarios that consist of the virtual scene of the VR game “Richie’s plank experience” and the realistic stimulus of hitting by basketball are adopted in the experiment. The synchronized eye movement (EMO), pupil, and electrocardiogram (ECG) of 17 healthy subjects with an even mix of men and women are recorded for FoH analysis. The observations on the FoH analysis carried out in the paper are two-fold: (1) The recognition ability of multi-modal physiological signals for HoF has been evaluated using ReliefF feature selection algorithm. The results show that pupil feature based model generally achieves the best classification performance for FoH. Multiple features consisted of pupil diameter, power spectral density (PSD) of EMO, pupil, ECG, and mean value of EMO can effectively quantify the variability. (2) The FoH analysis model built on the correlation analysis algorithms combined with multi-modal feature-level fusion and strategy-level fusion methods can effectively overcome the drawbacks of conventional statistical models.

Significance The multiple types of efficient oxygen transport paths were demonstrated in high-mechanical-strength hepta-bore Ba0.5Sr0.5Co0.8Fe0.2O3-δ hollow fiber membranes. These types of paths play a prominent role in enhancing oxygen permeation fluxes (17.6 mL min−1 cm−2 at 1223 K) which greatly transcend the performance of state-of-the-art Ba0.5Sr0.5Co0.8Fe0.2O3-δ hollow fiber membranes, showing a good commercialization prospect. © 2017 American Institute of Chemical Engineers AIChE J, 63: 4273–4277, 2017

Underground pipelines suffered severe external breakage caused by excavation devices due to arbitral road excavation. Acoustic signal-based recognition has recently shown effectiveness in underground pipeline network surveillance. However, merely relying on recognition may lead to a high false alarm rate. The reason is that underground pipelines are generally paved along a fixed direction and excavations out of the region also trigger the surveillance system. To enhance the reliability of the surveillance system, the direction-of-arrival (DOA) estimation of target sources is combined into the recognition algorithm to reduce false detections in this paper. Two hybrid recognition algorithms are developed. The first one employs extreme learning machine (ELM) for acoustic recognition followed by a focusing matrix-based multiple signal classification algorithm (ELM-MUSIC) for DOA estimation. The second introduces a decision matrix (DM) to characterize the statistic distribution of results obtained by ELM-MUSIC. Real acoustic signals collected by a cross-layer sensor array are conducted for performance comparison. Four representative excavation devices working in a metro construction site are used to generate the signal. Multiple scenarios of the experiments are designed. Comparisons show that the proposed ELM-MUSIC and DM algorithms outperform the conventional focusing matrix based MUSIC (F-MUSIC). In addition, the improved DM method is capable of localizing multiple devices working in order. Two hybrid acoustic signal recognition and source direction estimation algorithms are developed for excavation device classification in this paper. The novel recognition combining DOA estimation scheme can work efficiently for underground pipeline network protection in the real-world complex environment.

Sea anemone-like zinc-cobalt (Zn-Co) oxysulfides grown on nickel foam were synthesized by a hydrothermal treatment for the first time as a battery-type electrode for hybrid supercapacitors. Such a binder-free sea anemone-like Zn-Co oxysulfide electrode displays improved electrochemical properties, which can be attributed to the unique sea anemone-like morphology and the partly replacement of oxygen with sulfur since sulfur possesses a lower electronegativity. As a result, the Zn-Co oxysulfide electrode exhibits a specific capacity of 645.5 C g−1 (1 A g−1) which is much higher than 386.0 C g−1 of Zn-Co oxide electrode at the same conditions. In addition, the Zn-Co oxysulfide electrode also demonstrates good cycling stability (76.1% capacity retention after 1000 cycles). According to these, the Zn-Co oxysulfide electrode is testified to be a promising faradaic electrode for energy storage.

In swarm robotics, motion coordination is being viewed as a whole system consists of multiple robots each with different velocity and path being optimized. This research focus on improve an optimization process called Particle Swarm Optimization (PSO) by altering the fitness function from static to dynamic. PSO has tendency to being trapped in local optima. By employing dynamic acceleration coefficient, the position of particles will be less dependent on the global best position if the global best fitness is found to be saturated in the region and hence release the swarm to another region for searching in other unexplored region. Nominal, dynamic and extreme scenarios were employed to test the performance between PSO and dynamic coefficient PSO. The different on expected optimization time between DCPSO and PSO for nominal condition is DCPSO lead only 0.26 second. While in dynamic condition the different is DCPSO lead 1.09 second. For extreme condition, the different become significant, which DCPSO lead 3.58 second.

Many prestressed concrete bridges exhibit increasing long-term deflections under heavy traffic loads. Cyclic creep from increasing heavy traffic has received gradually increasing attention ...

One-class classification (OCC) has attracted a lot of attentions for its strong applicability of outlier/anomaly detection in real-world applications. Among them, the mixed model based on the autoencoder (AE) is the commonly used OCC architecture. The recent random neural network based extreme learning machine (ELM) AE (ELM-AE) has been extended to OCC. But no constraints have been applied to the encoded feature in ELM-AE and the random parameters used in ELM-AE may result in meaningless features. In this paper, we propose a novel within-class scatter constraint based randomized AE (WSC-RAE) by introducing a constraint on the distribution of the encoded feature to restrict the solution space and thus avoid the meaningless features. The proposed WSC-RAE is then embedded into a multilayer one-class ELM (ML-OCELM) framework for OCC. Experiments on 17 benchmark datasets are carried out to demonstrate the superiority of WSC-RAE.

One of the salient features of the extreme learning machine (ELM) is its fast learning speed. However, in a big data environment, the ELM still suffers from an overly heavy computational load due to the high dimensionality and the large amount of data. Using the alternating direction method of multipliers (ADMM), a convex model fitting problem can be split into a set of concurrently executable subproblems, each with just a subset of model coefficients. By maximally splitting across the coefficients and incorporating a novel relaxation technique, a maximally split and relaxed ADMM (MS-RADMM), along with a scalarwise implementation, is developed for the regularized ELM (RELM). The convergence conditions and the convergence rate of the MS-RADMM are established, which exhibits linear convergence with a smaller convergence ratio than the unrelaxed maximally split ADMM. The optimal parameter values of the MS-RADMM are obtained and a fast parameter selection scheme is provided. Experiments on ten benchmark classification data sets are conducted, the results of which demonstrate the fast convergence and parallelism of the MS-RADMM. Complexity comparisons with the matrix-inversion-based method in terms of the numbers of multiplication and addition operations, the computation time and the number of memory cells are provided for performance evaluation of the MS-RADMM.

Recently, by employing the stacked extreme learning machine (ELM) based autoencoders (ELM-AE) and sparse AEs (SAE), multilayer ELM (ML-ELM) and hierarchical ELM (H-ELM) has been developed. Compared to the conventional stacked AEs, the ML-ELM and H-ELM usually achieve better generalization performance with a significantly reduced training time. However, the ${\ell _1}$-norm based SAE may suffer the overfitting problem and it is unable to provide analytical solution leading to long training time for big data. To alleviate these deficiencies, we propose an enhanced H-ELM (EH-ELM) with a novel random sparse matrix based AE (SMA) in this paper. The contributions are in two aspects, 1) utilizing the random sparse matrix, the sparse features can be obtained; 2) the proposed SMA can provide an analytical solution so that the high computational complexity issue in SAE can be addressed. Experimental results on benchmark datasets show that the proposed EH-ELM achieves a higher recognition rate and a faster training speed than H-ELM and ML-ELM.

Along with the explosive growing of data, semi-supervised learning attracts increasing attention in the past years due to its powerful capability in labeling unlabeled data and knowledge mining. As an emerging method, the semi-supervised extreme learning machine (SSELM), that builds on ELM, has been developed for data classification and shown superiorities in learning efficiency and accuracy. However, the optimization of SSELM as well as most of the other ELMs is generally based on the mean square error (MSE) criterion, which has been shown less effective in dealing with non-Gaussian noises. In this paper, a robust regularized correntropy criterion based SSELM (RC-SSELM) is developed. The optimization of the output weight matrix of RC-SSELM is derived by the fixed-point iteration based approach. A convergent analysis of the proposed RC-SSELM is presented based on the half-quadratic optimization technique. Experimental results on 4 synthetic datasets and 13 benchmark UCI datasets are provided to show the superiorities of the proposed RC-SSELM over SSELM and other state-of-the-art methods.

As being increasingly used for underwater explorations, Autonomous Underwater Vehicles (AUVs) are diversified in their structures and outlines to match up with various mission requirements, which brings challenges to the design of customized submerged docking stations. Aiming at providing a generalized solution, this paper proposes an omnidirectional and positioning-tolerant planar type AUV docking and charging platform, which has no constraints on AUV structures. In order to solve the planar-type docking issues, an integrated visual navigation and docking algorithm is developed for the miniaturized prototype AUV. The adopted monocular Simultaneous Localization and Mapping (SLAM) program takes charge of the wide-range underwater locationing, and handles practical problems such as scale drift and tracking failure. A combined control strategy of horizontal dynamic positioning and vertical visual servo docking is developed for the terminal docking process. The strategy is robust against constant water flow disturbance and performs stable docking using a roll motion adjustment. The visual navigation and docking performances and other system functions are successfully validated in simulation, water pool experiment, and sea trial.

In the present work, photoactive cation N, N′-Dimenthyl-9, 9’-bisacridinium nitrate (BNMA) was assembled with exfoliated layered α-zirconium phosphate (α-ZrP) via an electrostatic layer-by-layer (LBL) assembly method. As a result, the luminescent films which were well-aligned and periodical had been successfully fabricated. Surprisingly, the lifetimes of (BNMA/ZrP)n were found to be prolonged by 16-fold for the first time, due to the isolation effect of inorganic nanosheets and hydrogen ion migration between the interlayers. Therefore, it is testified that α-ZrP can be used as the laminate and has remarkable influences on enhancing the lifetimes of chromophores. We expect that this new discovered effect can enable α-ZrP a kind of new potential material to develop novel light-emitting materials and optical devices.

Flower-like manganese-cobalt (Mn-Co) oxysulfide supported on Ni foam substrate is synthesized by two-step hydrothermal method as binder-free faradaic electrode for charge storage. The crystalline phase and morphology of as-fabricated materials are well-characterized by X-ray diffraction (XRD), Scan electron microscope (SEM), and X-ray photoelectron spectrum (XPS). The results demonstrate that the Mn-Co oxysulfide exists as polycrystal and sulfide are successfully partially substituted into Mn-Co oxide. Impressively, the Mn-Co oxysulfide electrode exhibits excellent electrochemical performance with an ultrahigh specific capacity of 490 C g −1 at 2 A g −1 and 415 C g −1 at 20 A g −1 which is much higher than 301 C g −1 at 2 A g −1 and 174 C g −1 at 20 A g −1 of the Mn-Co oxide nanosheets electrode. Moreover, the flower-like Mn-Co oxysulfide electrode also reveals good cycling stability with 86.5% capacity retention at high current density of 20 A g −1 after 3000 cycles. Because of the flower-like structure and partial substitution by sulfide, which provides large reaction surface area and more flexible structure, Mn-Co oxysufide electrode with excellent electrochemical performances would have great prospect for energy storage device applications.

The brittleness of perovskite hollow fiber membrane limits its large-scale application. A high mechanical strength Ba0.5Sr0.5Co0.8Fe0.2O3-δ nineteen-channel hollow fiber has been successfully prepared by a combined phase inversion and one-step thermal processing sintering technique. The crystalline structure, morphology, mechanical strength, oxygen permeation fluxes, transportation behavior and catalytic membrane reactor performance of the nineteen-channel hollow fiber were investigated systematically. The hollow fiber membrane of this configuration brings high mechanical strength (50.4 ± 1.3 N in 50 mm) reaching about 60 times that of the reported perovskite single-channel hollow fiber membranes and increased oxygen permeation(7.05 mL min-1cm-2 at 900 °C). Multiple types of oxygen transport paths were demonstrated and proved to have different contributions to the oxygen permeation flux. This new configuration will be easily exploited to build oxygen separator or membrane reactor, showing a prospect of commercialization.

In this work, a facile and feasible soft template method with the aid of buffer solution is successfully applied to synthesize high-order mesoporous cobalt tungstate for the first time. Attributing to the regulation of reaction solution's pH value and the existence of template, the phenomenon of phase transformation occurs, and high-order mesoporous structure is formed. Because of the variation of phase and morphology, only 448 mV can deliver a current density of 10 mA cm-2 with a small Tafel slope (61 mV dec-1) for mesoporous cobalt tungsten oxide hydroxide, while the cobalt tungstate nanoparticles cannot satisfy the basic demand of electrocatalysts. Herein, rational phase transformation and morphology design can significantly affect the property of oxygen evolution, which can provide vast opportunities to turn into candidates for the novel oxygen evolution catalyst.

Luminescent multilayer thin films (MTFs) based on exfoliated magnetic layered double hydroxides (LDHs) with/without oppositely-charged montmorillonite (MMT) nanosheets were fabricated via layer-by-layer self-assembly method. In this work, we chose transition metal-bearing LDHs nanosheets to offer magnetic field for the chromophores. At the same time, the oppositely-charged nanosheets can provide additional electronic microenvironment (EME). As a result, both EME and magnetic field have remarkable influences on enhancing the luminescent lifetimes of chromophores, which suggests a new pathway to develop the novel light-emitting materials and optical devices.

In this report, luminescent multilayer thin films (MTFs) based on oppositely-charged montmorillonite (MMT) and transition metal-bearing layered double hydroxides (LDHs) nanosheets were fabricated via a layer-by-layer assembly method. Oppositely-charged inorganic nanosheets can be expected to assemble an electronic microenvironment (EME), and transition metal-bearing LDHs nanosheets can offer an additional magnetic field (MF) for the chromophores at the same time. Surprisingly, the luminescent lifetimes of MTFs with EME and MF are significantly prolonged compared with that of the pristine chromophores, even much longer than those of MTFs without oppositely-charged and magnetic architecture. Therefore, it is highly expected that the EME and MF formed by oppositely-charged and transition metal-bearing inorganic nanosheets have remarkable influence on enhancing the luminescent lifetimes of chromophores, which suggest a new potential way to manipulate, control and develop the novel light-emitting materials and optical devices at molecular level.

A dynamic model of semi-batch three-dimensional electrode reactor was established based on the limiting current density, Faraday's law, mass balance and a series of assumptions. Semi-batch experiments of phenol degradation were carried out in a three-dimensional electrode reactor packed with activated carbon under different conditions to verify the model. The factors such as the current density, the electrolyte concentration, the initial pH value, the flow rate of organic and the initial organic concentration were examined to know about the pollutant degradation in the three-dimensional electrode reactor. The various concentrations and logarithm of concentration of phenol with time were compared with the dynamic model. It was shown that the calculated data were in good agreement with experimental data in most cases.

In this paper, we propose an intelligent network surveillance system to protect the urban underground pipelines from external damages caused by excavation devices. At each monitoring site, a microphone array is implemented for real-time acoustic collection and an intelligent excavation device recognition algorithm is embedded. A surveillance platform built on the fusion of multi monitoring sites is designed for a whole city. A novel statistical feature extraction method is first developed to mining the useful and representative information for the collected acoustic signals. Then, an artificial neural network trained by the popular extreme learning machine (ELM) and the regularized ELM (RELM) is used to perform the recognition of excavation devices in each monitoring site. To show the efficiency of the proposed system, experiments are conducted in this paper. Recognition performance on four most destructive devices is studied.

In many cities in the world, underground pipe network always suffers from serious external damage. The detection method of excavation devices based on acoustic signal has been extensively studied in the past research. It is important for preventing the destruction of urban underground pipeline network during disorderly underground excavation. However, the existing excavation devices detection methods have little attention to distance estimation, but only focus on the recognition results or direction-of-arrival (DOA) estimation. In the actual monitoring system, there may be frequent misinformation alarm, because of the lack of convincing distance-of-arrival (DisOA) estimation to achieve accurate source localization. In this paper, a new intelligent distance estimation algorithm, based on acoustic attenuation property for excavation devices localization, is brought forward. Specially, the extreme learning machine-based auto-encoder is used to obtain more robust feature representation from the acoustic signal frequency domain amplitude spectrum, and the regularized extreme learning machine (RELM) is utilized to train the regression model. Experimental results show that the proposed algorithm is effective.

Supercapacitors have been one of the highest potential candidates for energy storage because of their significant advantages beyond rechargeable batteries in terms of large power density, short recharging time, and long cycle lifespan. In this work, Cu-Co sulfides with uniform flower-like structure have been successfully obtained via a traditional two-step hydrothermal method. The as-fabricated Cu-Co sulfide vulcanized from precursor (P-Cu-Co sulfide) is able to deliver superior specific capacitance of 592 F g-1 at 1 A g-1 and 518 F g-1 at 10 A g-1 which are surprisingly about 1.44 times and 2.39 times higher than those of Cu-Co oxide electrode, respectively. At the same time, excellent cycling stability of P-Cu-Co sulfide is indicated by 90.4% capacitance retention at high current density of 10 A g-1 after 3000 cycles. Because of the introduction of sulfur during the vulcanization process, these new developed sulfides can get more flexible structure and larger reaction surface area, and will own richer redox reaction sites between the interfaces of active material/electrolyte. The uniform flower-like P-Cu-Co sulfide electrode materials will have more potential alternatives for oxides electrode materials in the future.

This work reviews the current progress of tight-binding methods and the recent edge-modified mean-field Hubbard model. Undercoordinated atoms and nonbonding electrons exist widely in nanomaterials and in network-structural materials with their impact under-estimated. A quantum theory was proposed to calculate the under-coordinated effects on the electronic structure of materials by incorporating bond order-length-strength (BOLS) correlation theory to mean-field Hubbard model, i.e. BOLS-HM. Consistency between the BOLS-HM calculation and density functional theory (DFT) calculation on 2D materials verified that i) bond contractions and potential well depression occur at the edge of graphene, phosphorene, and antimonene nanoribbons; ii) the physical origin of the band gap opening of graphene, phosphorene, and antimonene nanoribbons lays in the enhancement of edge potentials and hopping integrals due to the shorter and stronger bonds between undercoordinated atoms; iii) the band gap of 2D material nanoribbons expand as the width decreases due to the increasing under-coordination effects of edges which modulates the conductive behaviors; and iv) nonbond electrons at the edges and atomic vacancies of 2D material accompanied with the broken bond contribute to the Dirac-Fermi polaron (DFP) with a local magnetic moment.

In this paper, we investigate the dynamical behavior of a virus infection model with delayed humoral immunity. By using suitable Lyapunov functional and the LaSalleʼs invariance principle, we establish the global stabilities of the two boundary equilibria. If R 0 1 , the uninfected equilibrium E 0 is globally asymptotically stable; if R 1 1 R 0 , the infected equilibrium without immunity E 1 is globally asymptotically stable. When R 1 > 1 , we obtain the sufficient conditions to the local stability of the infected equilibrium with immunity E 2 . The time delay can change the stability of E 2 and lead to the existence of Hopf bifurcations. The stabilities of bifurcating periodic solutions is also studied. We check our theorems with numerical simulations in the end.

In this report, luminescent multilayer thin films (MTFs) based on functional molecules intercalated into layered double hydroxides (LDHs) and montmorillonite (MMT) nanosheets were fabricated by layer-by-layer assembly method (LBL method). Exfoliated LDHs and MMT nanosheets with opposite charges can be expected to provide an electronic microenvironment (EME) for chromophores, which are not found in previous literatures, and to offer the inorganic rigid building blocks at the same time. Surprisingly, the lifetimes of MTFs with EME's architecture are significantly prolonged, compared with that of the pristine powder, even considerably longer than those of the MTFs without EME's architecture. Therefore, it is highly expected that the EME formed by oppositely-charged inorganic nanosheets has remarkable influence on enhancing the lifetimes of chromophores, which suggests a new potential method to develop novel light-emitting materials and optical devices.

Flower-like Ni0·85Se nanosheets (Ni0·85Se-16 h) are synthesized by a two-step hydrothermal method. The electrochemistry performance and phase transformation of nickel selenides which obtained from different reaction times are examined. All the nickel selenides experience the phase transformation process. However, the opened flower-like Ni0·85Se-16 h sample takes the phase change process more slowly than others. In addition, the electrochemical properties differences between flower-like Ni0·85Se-16 h and NiO are also studied. Ni0.85Se-16 h exhibits a areal capacitance of 3.35 F cm−2 at 4 mA cm−2 which is approximately 3.6-fold higher than that of NiO. Then, the hybrid supercapacitor is assembled by using Ni0·85Se-16 h as a positive electrode and activated carbon as a negative electrode. The hybrid configuration could acquire a high energy density of 44.2 Wh kg−1 at a power density of 888.9 W kg−1, and 28.5 Wh kg−1 at 12825 W kg−1. The above results emphasize the fact that the opened flower-like construction could effectively promote the electron/ion transportation in the interface between the electrolyte and the active materials which delay the phase transition process and prove the fact that selenization process could enhance the areal capacitance more effectively than oxidation process. Thus, make it a potential candidate as battery-like electrode materials for hybrid supercapacitors.

In this paper, we investigate the dynamical behavior of a virus infection model with general incidence rate and humoral immunity. By using suitable Lyapunov functional and the LaSalle's invariance principle, we establish the global stability of the three equilibria. The uninfected equilibrium E 0 is globally asymptotically stable if R 0 ≤ 1, the infected equilibrium without immunity E 1 is globally asymptotically stable if R 1 ≤ 1 and R 0 > 1, the infected equilibrium with humoral immunity E 2 is globally asymptotically stable if R 1 > 1. We check our theorems with numerical simulation in the end.

Acoustic signal based recognition for excavation devices has been investigated in the past due to its significance in preventing the underground cables from being destroyed during the ground excavation. However, existing excavation devices classification algorithms have paid little attention to the reduction of background noises which usually severely degrade the recognition performance. This paper utilizes a cross microphone array to record the acoustic signals of excavation devices, which are then filtered by the minimum variance distortionless response (MVDR) beamforming algorithm to reduce the environment noises and enhance the desired signals. The filtered signals are then fed into the feature extraction and classifier learning. To show the effectiveness of the proposed method, we collected the real acoustics of two representative devices in a construction site to make the performance testing. Experiments show that, compared with conventional recognition methods the performance of the proposed method is significantly improved.

Research on the virtual machining environment is one of the key parts of virtual manufacturing technology. According to the features of virtual turning, this paper proposes a simplified Whitted lighting model based on the analysis of Phong and other local illumination models. Moreover, to obtain natural lighting effects, a local ray tracing algorithm is given to calculate the light intensity of every position during the course of simulation. This method cannot simulate the machining environment accurately, but also can reduce the calculation time. Further, to obtain a more realistic machining scene, the paper deduces the transfer matrix from the chip coordinate to the feeding coordinate so that the chip model can be integrated into the whole machining scene. Finally, an example of a virtual cutting scene is shown to demonstrate the effects of the global illumination model and chip simulation. The data needed for the construction of the scene and for chip simulation is obtained from a C616 lathe. This study plays an important role in richening virtual manufacturing theory and promoting the development of advanced manufacturing technology.

In this present report, luminescent ordered multilayer thin films (OMFs) based on oppositely-charged inorganic nanosheets and the different oppositely-charged chromophores were fabricated via layer-by-layer assembly method. Exfoliated layered double hydroxides (LDHs) and montmorillonite (MMT) nanosheets with opposite charges can be expected to provide a pseudo electronic microenvironment (PEM) which has not been declared in previous literatures, and transition metal-bearing LDHs nanosheets can offer an additional ferromagnetic effect (FME) for the chromophores at the same time. Surprisingly, the luminescent lifetimes of those OMFs with PEM and FME are significantly prolonged compared with that of the pristine chromophores, even much longer than those of OMFs without oppositely-charged and ferromagnetic architecture. Therefore, it is highly expected that the PEM and FME formed by oppositely-charged and transition metal-bearing inorganic nanosheets have remarkable influence on obtaining better optical property, which suggests a new potential way to manipulate, control and develop the novel light-emitting materials and optical devices.

The driving fatigue related human vocal organ changes can influence the strange atrractor's trajectories in phase space under a speech nonlinear dynamic model and correspondingly affect the chaos and fractal features of speech signal. Therefore, this paper proposes a method for detecting driver fatigue using nonlinear speech processing techniques combined with fuzzy support vector machine (SVM). First, we reconstructed the speech signal in phase space to build a speech nonlinear dynamic model, extracted and analyzed the changes of several nonlinear dynamic features such as the largest Lyapunov exponent, fractal dimension and approximate entropy when the degree of human fatigue changes. Then during the designing process of the multi-feature fatigue classifier, fuzzy clustering concept was introduced to the traditional SVM classifier, and the fuzzy membership degrees of the training speech samples are computed by a novel proposed membership function to overcome the sensitivity to noise and outliers. After training this fuzzy SVM, a multi-feature fusion classifier was established for fatigue recognition of drivers' speech samples. The experimental results confirm the feasibility and the effectiveness of this proposed method, and it seems to be promising in the application of driver fatigue detection.

Layered double hydroxides (LDHs), luminescent π-conjugated anionic polymer and montmorillonite (MMT) were orderly assembled into luminescent multilayer films via layer-by-layer self-assembly method. The electronic microenvironment (EME), the structure of which is like a traditional capacitor, can be constructed by exfoliated LDHs or MMT nanosheets. In addition, the rigid inorganic laminated configuration can offer stable surroundings between the interlayers. As a result, we conclude that EME can extend the luminescent lifespans of multilayer films substantially, due to affecting relaxation times of π-conjugated anionic polymer. Consequently, because of the remarkable impact on better photoemission behaviors of luminescent π-conjugated anionic polymer, EME assembled by LDHs or MMT nanosheets have had high hopes attached to them. They are expected to have the potential for designing, constructing, and investigating novel light-emitting thin films.