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The recent discovery of superconductivity near room temperature in highly compressed superhydrides highlights the key role of the hydrogen cage structure in determining the high superconducting critical temperature. Here, we propose a hitherto unknown metastable cage boron allotrope possessing high superconductivity at ambient pressure. Using first-principles calculations with structure searching, we have predicted various dynamically stable structures for MnB12 that can be possibly synthesized at high pressures and high temperatures. Among these structures the tetragonal t-MnB12 contains unique B16 cages, which are linked to each other to form open channels filled with Mn atoms. The removal of Mn atoms leads to the formation of a tetragonal cage boron allotrope t-B, which possesses partial ionicity due to the slight charge transfer between two inequivalent B atoms. t-B is a potential superconductor with an estimated critical temperature of 43 K, the highest value in elemental superconductors at ambient pressure.

Underwater image processing is an intelligence research field that has great potential to help developers better explore the underwater environment. Underwater image processing has been used in a wide variety of fields, such as underwater microscopic detection, terrain scanning, mine detection, telecommunication cables, and autonomous underwater vehicles. However, underwater imagery suffers from strong absorption, scattering, color distortion, and noise from the artificial light sources, causing image blur, haziness, and a bluish or greenish tone. Therefore, the enhancement of underwater imagery can be divided into two methods: 1) underwater image dehazing and 2) underwater image color restoration. This paper presents the reason for underwater image degradation, surveys the state-of-the-art intelligence algorithms like deep learning methods in underwater image dehazing and restoration, demonstrates the performance of underwater image dehazing and color restoration with different methods, introduces an underwater image color evaluation metric, and provides an overview of the major underwater image applications. Finally, we summarize the application of underwater image processing.

In order to produce a photocatalyst with increased visible photocatalytic ability, MIL-125 and melamine were selected as raw materials to prepare Z-scheme g-C3N4/TiO2 heterojunction photocatalyst using a simple calcination method. TEM, HRTEM, XRD, FTIR, EDS, and UV–vis absorption spectra were employed to investigate the prepared specimens. The visible-light catalytic property was examined via the degradation of methylene blue (MB). The recombination and separation activity of electrons and holes (h+/e−) were explored by the transient photocurrent response (TPR) and PL spectra. In contrast with plain TiO2 and g-C3N4, the g-C3N4/TiO2 photocatalyst exhibited increased photocatalytic activities when exposed to visible-light irradiation. With the addition of g-C3N4 at 8 wt%, the Z-scheme g-C3N4/TiO2 heterojunction performed best in the photocatalytic test toward MB dye at a degradation rate of 97.7%. Under visible-light irradiation, the Z-scheme heterojunction between g-C3N4 and TiO2 enables the high-efficient segregation of photogenic electrons and holes (h+/e−), leading to the increased photocatalytic ability. Meanwhile, the large specific surface area of the composite photocatalyst is conducive to the adsorption of the contaminant on the catalyst surface, which has an important effect on the catalytic reaction.

In multivariate chaotic time series prediction, correlation analysis is important for reducing input dimensions and improving prediction performance. Grey relational analysis (GRA) has proved to be an effective method for data correlation analysis, especially for inexact data and incomplete data. In GRA, points are usually regarded as objects, and the distance between points or the concave and convex degree are mostly used to measure the correlations. However, with discrete variables, correlation analysis results always tend to have some deviations when using prior GRA methods. Furthermore, GRA methods cannot directly use vector datasets. Therefore, in this paper, an improved GRA method is proposed based on vector projections. The input and output variables are expressed as vectors by linking two adjacent points. The vectors, instants of the points, are regarded as the objects, and the projection length of input variables to output variables is used to measure the correlations. The smaller the difference between the projection length and the input variables, the higher the correlation. Then, a hybrid variable selection and prediction model is proposed based on the improved GRA method for multivariate chaotic time series predictions, in order to overcome the negative effects of irrelevant and redundant variables caused by phase-space reconstruction. The experimental results based on the gas furnace dataset and San Francisco river runoff dataset demonstrate that the improved GRA method is effective for data correlation analysis, and the prediction accuracy is better than prior GRA-based methods.

High-dimensional and large-scale time series processing has aroused considerable research interests during decades. It is difficult for traditional methods to reveal the evolution state in dynamical systems and discover the relationship among variables automatically. In this paper, we propose a unified framework for nonuniform embedding, dynamical system revealing, and time series prediction, termed as Structured Manifold Broad Learning System (SM-BLS). The structured manifold learning is introduced for nonuniform embedding and unsupervised manifold learning simultaneously. Graph embedding and feature selection are both considered to depict the intrinsic structure connections between chaotic time series and its low-dimensional manifold. Compared with traditional methods, the proposed framework could discover potential deterministic evolution information of dynamical systems and make the modeling more interpretable. It provides us a homogeneous way to recover the chaotic attractor from multivariate and heterogeneous time series. Simulation analysis and results show that SM-BLS has advantages in dynamic discovery and feature extraction of large-scale chaotic time series prediction.

Spatio-temporal series prediction has attracted increasing attention in the field of meteorology in recent years. The spatial and temporal joint effect makes predictions challenging. Most of the existing spatio-temporal prediction models are computationally complicated. To develop an accurate but easy-to-implement spatio-temporal prediction model, this paper designs a novel spatio-temporal prediction model based on echo state networks. For real-world observed meteorological data with randomness and large changes, we use a cubic spline method to bridge the gaps between the neighboring points, which results in a pleasingly smooth series. The interpolated series is later input into the spatio-temporal echo state networks, in which the spatial coefficients are computed by the elastic-net algorithm. This approach offers automatic selection and continuous shrinkage of the spatial variables. The proposed model provides an intuitive but effective approach to address the interaction of spatial and temporal effects. To demonstrate the practicality of the proposed model, we apply it to predict two real-world datasets: monthly precipitation series and daily air quality index series. Experimental results demonstrate that the proposed model achieves a normalized root-mean-square error of approximately 0.250 on both datasets. Similar results are achieved on the long short-term memory model, but the computation time of our proposed model is considerably shorter. It can be inferred that our proposed neural network model has advantages on predicting meteorological series over other models.

Objective: This study aimed to explore the association between single nucleotide polymorphisms (SNPs) of nucleotide-binding oligomerization domain protein 2 (NOD2) gene and Parkinson's disease susceptibility, including IVS4 + 10A > C (rs72796353) and a missense mutation at exon 9 (c.2857A > G p.K953E). Methods: Rs72796353 and c.2857A > G p.K953E polymorphisms of NOD2 gene were genotyped via polymerase chain reaction-restriction fragment length polymorphism in 125 cases with PD and 120 healthy controls. Genotype and allele frequencies differences of gene polymorphisms between the case and control groups were analyzed by the Chi-square test. Odds ratio (OR) and 95% confidence interval (95%CI) were used to indicate the relative susceptibility to PD. Furthermore, Hardy-Weinberg equilibrium (HWE) was evaluated by the χ2 test in controls. Results: Neither genotypes nor allele of rs72796353 was significantly different in cases and control groups (p > .05). Differently, AG/GG genotype of NOD2 2857 A > G polymorphism were associated with the increased risk of PD (OR = 2.486, 95%CI = 1.223-5.056), and G allele carriers were 2.563 times risk to suffer from PD (OR = 2.563, 95%CI = 1.310-5.013). Besides, AG genotype might be also a risk factor for PD. Conclusion: NOD2 c.2857A > G p.K953E polymorphism may be correlated with PD susceptibility in Chinese Han population, but not rs727796353. Further study should be conduct to certify this conclusion.

For the development of high-performance spintronic nanodevices, one of the most urgent and challenging tasks is the preparation of two-dimensional materials with room-temperature ferromagnetism and a large magnetic anisotropic energy (MAE). Through first-principles swarm-intelligence structural search calculations, we identify an ideal ferromagnetic Fe3P monolayer, in which Fe atoms show a perfect Kagome lattice, leading to strong in-plane Fe-Fe coupling. The predicted Curie temperature of Fe3P reaches ∼420 K, and its MAE is comparable to those of ferromagnetic materials, such as Fe and Fe2Si. Moreover, the Fe3P monolayer remains as an above room-temperature ferromagnet under biaxial strains as large as 10%. Its lattice can be retained at temperatures of ≤1000 K, exhibiting a high thermodynamic stability. All of these desirable properties make the Fe3P monolayer a promising candidate for applications in spintronic nanodevices.

State space reconstruction is the foundation of chaotic system modeling. Selection of reconstructed variables is essential to the analysis and prediction of multivariate chaotic time series. As most existing state space reconstruction theorems deal with univariate time series, we have presented a novel nonuniform state space reconstruction method using information criterion for multivariate chaotic time series. We derived a new criterion based on low dimensional approximation of joint mutual information for time delay selection, which can be solved efficiently through the use of an intelligent optimization algorithm with low computation complexity. The embedding dimension is determined by conditional entropy, after which the reconstructed variables have relatively strong independence and low redundancy. The scheme, which integrates nonuniform embedding and feature selection, results in better reconstructions for multivariate chaotic systems. Moreover, the proposed nonuniform state space reconstruction method shows good performance in forecasting benchmark and actual multivariate chaotic time series.

Visual saliency (VS) is an important mechanism for defining which areas of an image will attract more attention of the HVS. Thus, VS can be employed to weight the just noticeable difference (JND) with different attention levels. Some VS-based JND profiles have been proposed in the DCT domain, which used the bottom-up features, such as luminance and texture only. Recently, the research about saliency detection has shown that a better saliency model considering both bottom-up features and top-down features will lead to a significant improvement for the overall saliency detection performance. In this paper, we propose a novel two-layer VS-induced JND profile, which is composed of the bottom-up features and the top-down feature extracted from DCT blocks in the transformed domain. In this model, the luminance and texture features are adapted to calculate the bottom-up features maps, while the top-down feature of focus is used to guide the generation of final salient regions since the camerapersons are used to facilitate the attention regions in focus. The proposed two-layer saliency-induced JND model is further applied to modulate the quantization step in the watermarking framework, which can make full use of its individual merits to achieve a better tradeoff between fidelity and robustness. The experimental results show that the proposed scheme has superior performance than the previous watermarking schemes.

The search for two-dimensional superconductors has attracted increasing interest because of their potential applications in constructing nanoscale superconducting devices. Through swarm-intelligence based CALYPSO method and the first-principles calculations, we have identified a monolayer structure for yttrium sulfide (t-YS), which is energetically and dynamically stable. The application of biaxial strain turns t-YS to a Bardeen–Cooper–Schrieffer superconductor, which mainly originates from the softening of in-plane modes of Y atoms. The superconducting critical temperature increases monotonously with strain, which reaches 6 K at a maximum strain of 10%. Calculations show that doping at 0.3 holes per unit cell based on a strain of 10% could further enhance the superconductivity to 7.3 K. Simulations have helped to propose a candidate substrate with ∼8.3% lattice mismatch to obtain superconductive t-YS experimentally. The findings will enrich two-dimensional superconductors and stimulate immediate experimental interest.

Pressure is a fundamental tool that can induce structural and electronic transformations, which is helpful to search for exotic materials not accessible at ambient conditions. Here, we have performed an extensive structural study on cubic Mg3As2 in a pressure range of 0–100 GPa by using a combination of structure predictions and first-principle calculations. Interestingly, two novel structures with space groups C2/m and P were uncovered that become energetically most stable at pressures of 12 GPa and 30 GPa, respectively. Phonon dispersions demonstrate that the three phases are dynamically stable in their respective low-enthalpy pressure ranges. The electronic calculations show that Mg3As2 keeps semiconductor properties at pressures up to 100 GPa. The interesting thing is that the direct semi-conducting property of Mg3As2 transforms into indirect semi-conducting when the pressure is above 12 GPa. The current results provide new insights for understanding the behavior of Mg3As2 at high pressures.

Materials possessing both superhard and metallic properties are beneficial for the creation of multifunctional devices under extreme conditions. Here, we report the formation of a new metallic superhard boron nitride at high pressure with stoichiometry B2N3 through first-principles calculations and structure searching. At ambient pressure, B2N3 has layered structures (h-B2N3) consisting of hexagonal B4N4 layers intercalated by triply bonded N2 molecules. With the pressure increasing to ∼10 GPa, h-B2N3 transforms to a three-dimensional tetragonal structure (t-B2N3) with the formation of single N–N bonds. Calculations reveal that t-B2N3 can be recovered under ambient conditions in view of the dynamical, thermal and mechanical stability. Interestingly, t-B2N3 is proposed to be a superhard material with an estimated Vicker's hardness of ∼52 GPa by performing stress–strain calculations. More importantly, electronic calculations show unique two-dimensional metallicity in t-B2N3, which originates from the π orbitals of N–N bonds spreading in the ab plane. In addition, the energy density of ∼2.95 kJ g−1 makes t-B2N3 a potential high-energy density material.

Motivated by the idea of 'decomposition and ensemble', this paper proposes a novel method based on the wavelet-denoising algorithm and multiple echo state networks to improve the prediction accuracy of noisy multivariate time series. The noisy time series is first denoised by a wavelet soft thresholding algorithm and decomposed into a set of well-behaved constitutive series. Each constitutive series is then predicted by a separate echo state network with proper parameters that match the specified dynamics. Finally, the overall prediction is achieved by a linear combination of the constitutive series. For each constitutive series, we use the correlation integral method to select the phase-reconstruction parameters and to construct the appropriate input. Two sets of multivariate time series are investigated using the proposed model and some other related work. The simulation results demonstrate the effectiveness of the proposed method.

A novel β-cyclodextrin pendant polymer (ε-PL-CD), composed of poly(ε-lysine) (ε-PL) main chain and glycine-β-cyclodextrin (Gly-CD) side chains, was prepared by a simple two-step procedure. The ε-PL-CD was investigated as a drug carrier of hydrophobic drug scutellarin (SCU). The characterization and complexation mode of the SCU:ε-PL-CD were researched in both solution and solid state by means of photoluminescence spectroscopy, 1H and 2D NMR, X-Ray powder diffraction (XRPD), thermal gravimetric analysis, Particle size and Zeta potential. The solubility test indicated that the solubilizing ability of SCU:ε-PL-CD was significantly improved compared with SCU:β-CD and free SCU. Besides, in vitro cell experiment, it was found that SCU:ε-PL-CD has a strong inhibitory effect on the growth and invasion of tumor cells. The present study provides useful information for ε-PL-CD as a drug carrier material.

In order to solve the problem of the speech recognition, this paper proposes a novel framework to process speech signals by using the deep fully convolutional neural network (DFCNN) as the acoustic model and the Transformer as the linguistic model. DFCNN can learn more about speech signals through a deep network structure, while Transformer's self-attention mechanism makes it an efficient linguistic model. In order to better extract the characteristic information of speech signal, we transform the speech signal into a two-dimensional language spectrum by frame division and Fourier transform. In this way, the language spectrum can be processed by using convolutional neural network, which has promising ability to process two-dimensional data. The output of the acoustic model is passed into the linguistic model, and a complete sentence is output. At the same time, considering that there are many hyper parameters in the model, in order to adjust these hyper parameters efficiently, we employ the Bayesian optimization algorithm to optimize the hyper parameters of the acoustic model and the linguistic model, so as to further improve the accuracy of speech recognition. Experimental results on speech recognition library THCHS30 show that compared with other models, the optimized model has higher recognition rate for audio and stronger generalization ability.

Time series prediction is an active area and attracts the attention of researchers from lots of fields. With the expansion of the application of time series forecasting in the real world, many traditional methods that predicted time series with a few data are no longer applicable. Motivated by the demand of accurate prediction, we come up with the piecewise time series prediction model combining stacked long short-term memory network with genetic algorithm. Stacked long short-term memory network (LSTM) is an advanced sequence learning technology. We divide the time series into several segments and then use the stacked LSTM trained on the previous segment to predict the next segment to realize online prediction. The parameters of the stacked LSTM for each segment prediction are optimized by improved genetic algorithm. For purpose of demonstrating the feasibility and effectivity of the proposed model, we carry out simulation experiments on basis of a hybrid benchmark chaotic time series and a real-world dataset of the hourly ozone concentration. Experimental results demonstrate that the proposed model can automatically select the proper structure according to the data, which avoids the structure being too redundant or too simple. Furthermore, it can improve the prediction accuracy of time series and has good practicability.

The design of two-dimensional superconductors has attracted great research interest owing to their wide application in nanoscale devices. Here, we combine first-principles calculations with structure searching technology to identify a unique stable hexagonal $h\text{\ensuremath{-}}{\mathrm{MnB}}_{3}$ monolayer, exhibiting a slightly higher energy with respect to the reported tetragonal $t\text{\ensuremath{-}}{\mathrm{MnB}}_{3}$. Interestingly, $h\text{\ensuremath{-}}{\mathrm{MnB}}_{3}$ contains two boron kagome layers sandwiched with Mn atoms. It exhibits metallic properties and has a superconducting transition temperature of 24.9 K, which is much higher than 2.9 K in $t\text{\ensuremath{-}}{\mathrm{MnB}}_{3}$. Its superconductivity mainly originates from the coupling between in-plane vibrational phonons of boron kagome layers and electrons of Mn atoms. $h\text{\ensuremath{-}}{\mathrm{MnB}}_{3}$ exhibits a tunable superconductivity, and reaches a maximum of 34 K at 2% tensile strain resulting from the softening in-plane modes of boron kagome layers. The Si (111) surface may be an ideal substrate for the growth of superconductive $h\text{\ensuremath{-}}{\mathrm{MnB}}_{3}$. The unique superconducting mechanism observed here could inspire the searching of more boron kagome based two-dimensional superconductors.

Uranus and Neptune are generally assumed to have helium only in their gaseous atmospheres. Here, we report the possibility of helium being fixed in the upper mantles of these planets in the form of NH3–He compounds. Structure predictions reveal two energetically stable NH3–He compounds with stoichiometries (NH3)2He and NH3He at high pressures. At low temperatures, (NH3)2He is ionic with NH3 molecules partially dissociating into (NH2)− and (NH4)+ ions. Simulations show that (NH3)2He transforms into intermediate phase at 100 GPa and 1000 K with H atoms slightly vibrate around N atoms, and then to a superionic phase at ~2000 K with H and He exhibiting liquid behavior within the fixed N sublattice. Finally, (NH3)2He becomes a fluid phase at temperatures of 3000 K. The stability of (NH3)2He at high pressure and temperature could contribute to update models of the interiors of Uranus and Neptune., Helium is generally considered too inert to be present in giant ice planet mantles. The authors, by first-principles calculations and crystal structure searches, find stable ammonia–helium compounds at the conditions of Uranus and Neptune’s upper mantles, with possible implications in the planet composition models.

Searching for experimentally feasible intrinsic quantum anomalous Hall (QAH) insulators is of great significance for dissipationless electronics applications. Here we predict, based on density functional theory (DFT), that four monolayer transition metal tri-bromides (VBr3, FeBr3, NiBr3, and PdBr3) are endowed with intrinsic half-metallicity and possess quantum anomalous Hall insulating phases. DFT+U calculations reveal that the VBr3, NiBr3, and PdBr3 monolayers undergo nontrivial to Mott insulator transitions with increasing on-site Hubbard Coulomb interaction U at 0.5, 2 and 3 eV. The gap opening induced by the spin-orbit coupling drives the systems into the QAH state. The Curie temperatures of the VBr3, NiBr3, and PdBr3 monolayers are ∼190, 100 and 110 K. Additionally, the calculated cleavage energies suggest that the freestanding VBr3, FeBr3, NiBr3, and PdBr3 monolayers can be easily produced by exfoliation using adhesive tape, which may stimulate experimental research interest to achieve QAH phases.

Accurate prediction of solar irradiance is beneficial in reducing energy waste associated with photovoltaic power plants, preventing system damage caused by the severe fluctuation of solar irradiance, and stationarizing the power output integration between different power grids. Considering the randomness and multiple dimension of weather data, a hybrid deep learning model that combines a gated recurrent unit (GRU) neural network and an attention mechanism is proposed forecasting the solar irradiance changes in four different seasons. In the first step, the Inception neural network and ResNet are designed to extract features from the original dataset. Secondly, the extracted features are inputted into the recurrent neural network (RNN) network for model training. Experimental results show that the proposed hybrid deep learning model accurately predicts solar irradiance changes in a short-term manner. In addition, the forecasting performance of the model is better than traditional deep learning models (such as long short term memory and GRU).

Just noticeable distortion (JND) is widely employed to describe the perception redundancy in the quantization-based watermarking framework. However, the existing JND models are generally constructed to treat every region of the photograph with an equal focus level, whereas the defocus effect has never been considered. In this paper, the defocus feature, which can portray the aesthetic emphasis in the photograph, is provided to improve the perceptual JND model. Firstly, two indicators which consider the block energy in the defocus measurement (DM) are proposed. Then, the defocus feature map (DFM) is obtained by integrating the influence of the circumambient blocks, and it is applied to the proposed JND contrast masking (CM) processing. In this way, a new blind photograph watermarking method, with emphasis on defocus-JND estimation combined with the proposed CM, is presented. Simulations show that the proposed JND is more suitable for watermarking framework than some exiting JND models, and the proposed watermarking scheme with the improved defocus-based JND model has superior robustness compared with some watermarking schemes.

Crystalline silicon is dominating the current solar cell market due to the significant efficiency improvement and cost reduction in last decades. However, its indirect band gap nature leads to inefficient visible-light absorption, which seriously impedes further performance enhancement in silicon-based photovoltaic devices. Thus, it is highly desirable to develop direct band gap silicon materials. Herein, by means of ab initio swarm-intelligence structure-searching method, we predicted a quasi-direct gap semiconducting tri-layer silicene structure consisting of alternating arrays of six-membered Si rings, which can be converted into a direct gap semiconductor of 0.86 eV by applying a low tensile strain (~ 2.5%). Our calculations revealed that the photovoltaic efficiency of the tri-layer silicene reaches 29% at 1.0 µm, which is comparable to that of bulk GaAs with the highest conversion efficiency among thin-film solar cell absorbers.

Two-dimensional (2D) materials have recently attracted significant interest in condensed-matter physics and materials science due to rich intriguing physics and potential applications. Here, by first-principles calculations, we discover a phase of transition-metal dichalcogenides (MoS2 and its family) named the 1H′ phase. We demonstrate that 1H′MoS2 monolayer has multiple topological phase transitions from a topologically trivial insulator state to a 2D type-II Dirac semimetal state, and then to a 2D ideal type-I semimetal state under a moderate tensile strain. The Dirac points are protected by the coexistence of space-inversion (P) and time-reversal (T) symmetry. Our results highlight a family of nonhoneycomb 2D Dirac semimetals with potential application in nanoelectronics.

Ground surface settlement forecasting in the process of tunnel construction is one of the most important techniques towards sustainable city development and preventing serious damages, such as landscape collapse. It is evident that modern artificial intelligence (AI) models, such as artificial neural network, extreme learning machine, and support vector regression, are capable of providing reliable forecasting results for tunnel surface settlement. However, two limitations exist for the current forecasting techniques. First, the data provided by the construction company are usually univariate (i.e., containing only the settlement data). Second, the demand of tunnel surface settlement is immediate after the construction process begins. The number of training data samples is limited. Targeting at the above two limitations, in this study, a novel ensemble machine learning model is proposed to forecast tunnel surface settlement using univariate short period of real-world tunnel settlement data. The proposed Adaboost.RT framework fully utilizes existing data points with three base machine learning models and iteratively updates hyperparameters using current surface point locations. Experimental results show that compared with existing machine learning techniques and algorithms, the proposed ensemble learning method provides a higher prediction accuracy with acceptable computational efficiency.

With the applications of Internet of Things (IoT) for smart cities, the real-time performance for a large number of network packets is facing serious challenge. Thus, how to improve the emergency response has become a critical issue. However, traditional packet scheduling algorithms cannot meet the requirements of the large-scale IoT system for smart cities. To address this shortcoming, this paper proposes EARS, an efficient data-emergency-aware packet scheduling scheme for smart cities. EARS describes the packet emergency information with the packet priority and deadline. Each source node informs the destination node of the packet emergency information before sending the packets. The destination node determines the packet scheduling sequence and processing sequence according to emergency information. Moreover, this paper compares EARS with a first-come, first-served, multilevel queue algorithm and a dynamic multilevel priority packet scheduling algorithm. Simulation results show that EARS outperforms these previous scheduling algorithms in terms of packet loss rate, average packet waiting time, and average packet end-to-end delay.

Monolayer MoS2 has long been considered as the most promising candidate for wearable photovoltaic devices. However, its photovoltaic efficiency is restricted by its large band gap (2.0 eV). Though the band gap can be reduced by increasing the number of layers, the indirect band gap nature of the resulting multilayer MoS2 is unfavorable. Herein, we report a theoretical discovery of the hitherto unknown symmetry-broken phase (denoted as 1Td) of monolayer MoS2 through a swarm structure search. The 1Td phase has a distorted octahedral coordinated pattern of Mo, and its direct band gap of 1.27 eV approaches the optimal value of 1.34 eV that gives the Shockley–Queisser limit for photovoltaic efficiency. Importantly, the direct band gap nature persists in thin films with multilayers owing to extremely weak vdW forces between adjacent 1Td layers. The theoretical photovoltaic efficiency at 30 nm thickness reaches ∼33.3%, which is the highest conversion efficiency among all the thin-film solar cell absorbers known thus far. Furthermore, several feasible strategies including appropriate electron injection and annealing methods were proposed to synthesize the 1Td phase. Once synthesized, the superior photovoltaic properties of the 1Td phase may lead to the development of an entirely new line of research for transition metal dichalcogenide solar cells.

Blending is a promising method to modify the ash-fusion characteristics of coal to meet gasification requirements. To investigate the variation in ash-fusion behavior for high ash-fusion-temperature (AFT) coal through coal blending and its regulating mechanism, the ash-chemistry characteristics of four coals (Nantun, Pengzhuang, Biaodian, and Xiangyuan) were analyzed. Mineral-transformation behaviors in the mixed ashes were investigated by X-ray powder diffractometry using normalized reference intensity ratio software, scanning electron microscopy analyzer equipped with energy-dispersive X-ray spectrometry, and FactSage software. Pengzhuang and Biaodian coals, AFTs meet entrained-flow gasifier requirements when the Xiangyuan mass ratio reaches 20%. The Nantun requires a Xiangyuan mass ratio of XY > 30% because of its high aluminum oxide content. The required Xiangyuan optimal blending mass ratio is 40%–70% for the three high AFT coals to meet gasification requirements in the entrained-flow gasifier. An increased Xiangyuan mass ratio leads to decreasing contents of high melting-point mullite and silica and increasing contents of low melting-point anorthite, hercynite, gehlenite, and hedenbergite. Amorphous matter in three mixed ash types resulted in a decrease in their AFTs. The variation in liquid phase under certain conditions reflects the change in AFT.

Microplastics were reported to adsorb antibiotics and may modify their effects on soil systems. But there has been little research investigating how microplastics may affect the toxicities of antibiotics to microbes under future climate conditions. Here, we used a free-air CO2 enrichment system to investigate the responses of soil microbes to sulfamethazine (SMZ, 1 mg kg−1) in the presence of polystyrene microplastics (PS, 5 mg kg−1) at different CO2 concentrations (ambient at 380 ppm and elevated at 580 ppm). SMZ alone decreased bacterial diversity, negatively affected the bacterial structure and inter-relationships, and enriched the sulfonamide-resistance genes (sul1 and sul2) and class 1 integron (intl1). PS, at both CO2 conditions, showed little effect on soil bacteria but markedly alleviated SMZ’s adverse effects on bacterial diversity, composition and structure, and inhibited sul1 transmission by decreasing the intl1 abundance. Elevated CO2 had limited modification in SMZ’s disadvantages to microbial communities but markedly decreased the sul1 and sul2 abundance. Results indicated that increasing CO2 concentration or the presence of PS affected the responses of soil microbes to SMZ, providing new insights into the risk prediction of antibiotics under future climate conditions.

In this paper, a novel method (Hybrid–ELM–PLSR) is proposed based on hybrid variable selection algorithm and improved extreme learning machine (ELM) for multivariate chaotic time series prediction. The hybrid variable selection algorithm combines the advantages of filter and wrapper, effectively balancing the calculation speed and prediction accuracy. Moreover, for ELM, multicollinearity, which can result in ill-condition, is always existent among the hidden layer output matrix. And the optimal number of hidden nodes is also difficult to be determined. Therefore,in order to overcome these problems, an improved ELM (ELM–PLSR) is proposed based on partial least square regression (PLSR). It can effectively enhance the stability performance and prediction performance of ELM. Hybrid–ELM–PLSR can be divided into three stages. At first, filter is used to rearrange the input variables through the correlations with desired variables. Then wrapper is used to select the optimal variable subset through evaluating the prediction performance of different subsets. Finally, ELM–PLSR is used to build the prediction model. The simulation experiment results based on San Francisco river runoff dataset demonstrate that the proposed method is effective for multivariate chaotic time series. And the prediction accuracy and reliability are higher than other methods.

The influences of two red mud (RM) samples (ZZ and ZM) on fusion behaviors of three high ash fusion temperature (AFT) coals (FZ, JC and JZ) were investigated. It was evaluated by determining AFTs of coals, RMs, and the mixtures of coal ash with RM, as well as their compositions of ashes and mineral. The results showed that the contents of CaO, Fe2O3, and Na2O in RMs were higher than those in coal ashes, which caused RMs’ AFTs lower than those of coals. The formation of high melting-point mullite might be the main factor causing coal AFTs higher. With the increase in RM mass ratio, the increasing amount of low-melting eutectic (anorthite (CaAl2Si2O8), fayalite (Fe2SiO4), and anorthite (Na-rich) (Ca,Na)(Si,Al)4O8)) and amorphous matter made AFTs lower. ZZ reduced AFTs obviously than ZM because the content of Na2O in ZZ was higher than that in ZM. Owing to lower ion potential of Na+ than those of Ca2+ and Fe2+, Na+ is easier to enter into the lattice of mullite and results in the reduction of AFTs.

Titanium dioxide has been widely used as an efficient transition metal oxide photocatalyst. However, its photocatalytic activity is limited to the ultraviolet spectrum range due to the large bandgap beyond 3 eV. Efforts to reduce the bandgap to achieve a broader spectrum range of light absorption have been successfully attempted via the experimental synthesis of dopant-free metastable surface structures of rutile-type TiO2 (011) 2 × 1. This new surface phase possesses a reduced bandgap of ∼2.1 eV, showing great potential for an excellent photocatalyst covering a wide range of visible light. There is a need to establish the atomistic structure of this metastable surface to understand the physical cause for the bandgap reduction and to improve the future design of photocatalysts. Here, we report computational investigations in an effort to unravel this surface structure via swarm structure-searching simulations. The established structure adopts the anatase (101)-like structure model, where the topmost 2-fol...

Motivated by the high superconducting transition temperature (TC) shown by monolayer FeSe on cubic perovskite SrTiO3(001) and SrTiO3(001)-2×1 reconstructed surfaces, in this study, we explore the atomic and electronic structures of monolayer FeSe on various SrTiO3(001)-2×1 surface reconstructions using the CALYPSO method and first-principles calculations. Our search reveals two new Ti2O2 and Ti2O reconstructed surface structures, besides the Ti2O3 and double TiO2 layer reconstructed surfaces, and the two new Ti2O2 and Ti2O reconstructed surface structures are more stable under Ti-rich conditions than under Ti-poor conditions. The Fermi-surface topology of an FeSe monolayer on Ti2O3- and Ti2O2-type reconstructed STO surfaces is different from that of an FeSe monolayer on a Ti2O-type STO reconstructed surface. The established structure of monolayer FeSe on a Ti2O-type STO(001) reconstructed surface can naturally explain the experimental observation of the electronic band structure on the monolayer FeSe superconductor and obtained electrons counting per Fe atom. Surface states in the mid-gap induced by various STO surface reconstructions will result in band bending. The surface-state-induced band bending is also responsible for the electron transfer from the STO substrate to the FeSe films.

The ash adhesion and deposition (AD)characteristics of three low-rank coal (LRC) were investigated by self-made AD analyzer, X-ray fluorescence spectrometry, scanning electron microscopy equipped with energy dispersive X-ray detector, and X-ray diffraction. Under a given condition, the deposition mass ratios of different LRC ashes are different, which mainly due to the difference in iron content. The mass ratios of ash deposition under oxidizing atmospheres are higher than those under reducing atmospheres because of the effects of different valence of iron. The mass ratio of AD increases with the increase of temperature, while it decreases with gas velocity increases. The possible formation mechanism of AD is proposed by adhesive amorphous matter formation, iron enrichment and the generation of ferro-aluminate and ferro-alumino silicate, substitution of Fe 2 + by Ca 2 + , and mullite adhesion.

Differential evolution for multi-objective optimization is a useful and straightforward evolutionary optimization algorithm. Since the algorithm randomly searches in problem space, it may get into trouble when we adapt it to solve multi-objective problems. With regard to the limitation of DE, we propose a new algorithm PSDE to solve multi-objective functions based on the PSO operator and DE operator. In this algorithm, we utilize DE operator to promote the uniform spread of solutions and use PSO operator to obtain good local search ability by retaining the best solutions obtained so far. Experimental results on a set of benchmark test functions sustain the effectiveness of the proposed algorithm.

A better tradeoff between robustness and invisibility will be realized by using the just noticeable (JND) model into the quantization-based watermarking scheme. The JND model is usually used to describe the perception characteristics of human visual systems (HVS). According to the research of cognitive science, HVS can adaptively extract the structure features of an image. However, the existing JND models in the watermarking scheme do not consider the structure features. Therefore, a novel JND model is proposed , which includes three aspects: contrast sensitivity function, luminance adaptation, and contrast masking (CM). In this model, the CM effect is modeled by analyzing the direction features and texture complexity, which meets the human visual perception characteristics and matches well with the spread transform dither modulation (STDM) watermarking framework by employing a new method to measure edge intensity. Compared with the other existing JND models, the proposed JND model based on structural regularity is more efficient and applicable in the STDM watermarking scheme. In terms of the experimental results, the proposed scheme performs better than the other watermarking scheme based on the existing JND models.

Hydrogen-rich materials have attracted great interest since the recent discovery of superconductivity at 203 K in highly compressed hydrogen sulfide. To probe the role of covalent bonding in determining the ${T}_{\mathrm{c}}$ of hydrogen-related superconductors, we systematically studied the crystal structure and superconductivity of ${\mathrm{H}}_{6}\mathrm{SSe}$, a hypothetical compound derived from ${\mathrm{H}}_{3}\mathrm{S}$ with half its S atoms replaced by group neighbor Se. First-principles structure searches identify three dynamically stable structures for ${\mathrm{H}}_{6}\mathrm{SSe}$ at 200 GPa. Interestingly, all three structures keep the main feature of the cubic $Im\overline{3}m$ structure of ${\mathrm{H}}_{3}\mathrm{S}$, but with different Se substitution positions. Electron-phonon coupling calculations reveal the superconductive potential of the three phases of ${\mathrm{H}}_{6}\mathrm{SSe}$, with ${T}_{\mathrm{c}}$ decreasing (from 195 to 115 K) upon the declining strength of the weakest covalent H-S or H-Se bonds in each structure, thereby highlighting the key role of covalent bonding in determining ${T}_{\mathrm{c}}$. For comparison, O-substituted ${\mathrm{H}}_{6}\mathrm{SO}$ was predicted to assume a semiconducting phase with entirely different structural features from ${\mathrm{H}}_{6}\mathrm{SSe}$. We attribute this difference to the much stronger electronegativity of O (3.44) compared with S (2.58) or Se (2.55).

Nitrogen (N)-doped graphitic carbons with one-dimensional hollow/porous structures were synthesized via a sacrificial template of CdTe@ZIF-8 nanofibers. The N-doped graphitic carbons exhibited better electrocatalytic activity for ORR based on higher diffusion-limited current density and more positive half-wave potential as compared to carbons derived from ZIF-8.

The broad learning system (BLS) is an emerging approach for effective and efficient modeling of complex systems. The inputs are transferred and placed in the feature nodes, and then sent into the enhancement nodes for nonlinear transformation. The structure of a BLS can be extended in a wide sense. Incremental learning algorithms are designed for fast learning in broad expansion. Based on the typical BLSs, a novel recurrent BLS (RBLS) is proposed in this paper. The nodes in the enhancement units of the BLS are recurrently connected, for the purpose of capturing the dynamic characteristics of a time series. A sparse autoencoder is used to extract the features from the input instead of the randomly initialized weights. In this way, the RBLS retains the merit of fast computing and fits for processing sequential data. Motivated by the idea of “fine-tuning” in deep learning, the weights in the RBLS can be updated by conjugate gradient methods if the prediction errors are large. We exhibit the merits of our proposed model on several chaotic time series. Experimental results substantiate the effectiveness of the RBLS. For chaotic benchmark datasets, the RBLS achieves very small errors, and for the real-world dataset, the performance is satisfactory.

Multivariate chaotic time series prediction is a hot research topic, the goal of which is to predict the future of the time series based on past observations. Echo state networks (ESNs) have recently been widely used in time series prediction, but there may be an ill-posed problem for a large number of unknown output weights. To solve this problem, we propose a hybrid regularized ESN, which employs a sparse regression with the ${L}_{1}/{2}$ regularization and the ${L}_{2}$ regularization to compute the output weights. The ${L}_{1}/{2}$ penalty shows many attractive properties, such as unbiasedness and sparsity. The ${L}_{2}$ penalty presents appealing ability on shrinking the amplitude of the output weights. After the output weights are calculated, the input weights, internal weights, and output weights are fine-tuning by a Hessian-free optimization method—conjugate gradient backpropagation algorithm. The fine-tuning helps to bubble up the input information toward the output layer. Besides, the largest Lyapunov exponent is used to calculate the predictable horizon of a chaotic time series. Experimental results on benchmark and real-world datasets show that our proposed method is superior to other ESN-based models, as sparser, smaller-absolute-value, and more informative output weights are obtained. All of the predictions within the predictable horizon of the proposed model are accurate.

Improving universality and robustness of the control method is one of the most challenging problems in the field of complex networks (CNs) synchronization. In this paper, a special unilateral coupling finite-time synchronization (UCFTS) method for uncertain CNs is proposed for this challenging problem. Multiple influencing factors are considered, so that the proposed method can be applied to a variety of situations. First, two kinds of drive-response CNs with different sizes are introduced, each of which contains two types of nonidentical nodes and time-varying coupling delay. In addition, the node parameters and topological structure are unknown in drive network. Then, an effective UCFTS control technique is proposed to realize the synchronization of drive-response CNs and identify the unknown parameters and topological structure. Second, the UCFTS of uncertain CNs with four types of nonidentical nodes is further studied. Moreover, both the networks are of unknown parameters, time-varying coupling delay and uncertain topological structure. Through designing corresponding adaptive updating laws, the unknown parameters are estimated successfully and the weight of uncertain topology can be automatically adapted to the appropriate value with the proposed UCFTS. Finally, two experimental examples show the correctness of the proposed scheme. Furthermore, the method is compared with the other three synchronization methods, which shows that our method has a better control performance.

To deal with “curse of dimensionality” caused by high-dimensional data and the underutilization of spatial information in the classification of hyperspectral imagery (HSI), a new spectral-spatial classification method based on probabilistic support vector machine (SVM) and guided filter is proposed. The proposed method utilizes spatial information in two aspects. Firstly, the extended morphological profiles (EMP), differential morphological profiles (DMP) and Gabor textural features of hyperspectral image are extracted, which can preserve the texture, outline and shape of the original image. The spectral and extracted spatial features are joint classified by probabilistic SVM to obtain the initial classification probabilistic maps. Secondly, the guided filter is used to regularize the initial classification probabilistic maps, which can enhance the smoothness of local neighbourhood and preserve the edge information. The proposed algorithm is examined by the Indian Pines and University of Pavia hyperspectral datasets. On the same experimental conditions, the proposed method achieves the highest classification accuracy and the most reasonable classification map, demonstrating the proposed algorithm has obvious advantages in hyperspectral remote image classification.

It has been known that human visual systems (HVSs) can be applied to describe the underlying masking properties for the image processing. In general, HVS can only perceive small changes in a scene when they are greater than the just noticeable distortion (JND) threshold. Recently, the cognitive resources of huma visual attention mechanisms are limited, which can not concentrate on all stimuli. To be specific, only more important stimuli will react from the mechanisms. When it comes to visual attention mechanisms, we need to introduce the visual saliency to model the human perception more accurately. In this paper, we presents a new wavelet-based JND estimation method that takes into account the interrelationship between visual saliency and JND threshold. In the experimental part, we verify it from both subjective and objective aspects. In addition, the experimental results show that extracting the saliency map of the image in the discrete wavelet transform (DWT) domain and then modulating its JND threshold is better than the non-modulated JND effect.

Recently, the scale of observed time series becomes bigger and bigger, and the learning ability of conventional models is limited to depict the characteristics of large-scale time series. We put forward a deep model that uses the sparse autoencoder to extract the characteristics of the time series layer by layer. Sparse autoencoder can provide a simple explanation of the data by means of a reduced dimensionality of parts and extract the hidden architecture in the data. By this method, on the one hand, the dimension of the matrix that describes the problem is reduced, on the other hand, a large number of data can be compressed and summarized. Then we calculate the output weights by the method of pseudo inverse. Experiment results on Lorenz series and PM2.5 concentration in Shanghai dataset demonstrate the effectiveness of the proposed method.

Kernel recursive least squares (KRLS) has become a new research hotspot, attracting researchers’ attention in the field of time series online prediction. As an online algorithm, its computational form is updating incrementally as a new sample achieved. But as the number of data samples increases, its computational complexity will increase. And there is a lack of ability to track time-varying characteristics in the online prediction process. Therefore, we add a sliding window approach to the sparse KRLS and propose a new improved algorithm, which is sparse kernel recursive least squares based on sliding window (SW-SKRLS). This improved algorithm can limit the computational complexity and perform better online prediction in time-varying environments. To prove the validity of the proposed algorithm, we make simulations on the Lorenz time series and Dalian monthly average temperature and rainfall time series. The simulation results show that this algorithm has good prediction accuracy and efficiency.

Extreme learning machines have attracted increasing attention for setting the input weights and hidden biases randomly. However, excessive hidden neurons in ELMs may cause ill-posed problem when Moore-Penrose generalized inverse method is used to train the neural network. It generates large amplitude of output weights, which leads to bad generalization. To solve this problem, we propose an improved multi-objective particle swarm optimization algorithm to optimize the output weights of ELMs. It consists of two objective functions. One is the sum of the errors between predicted values and target values. The other one is the $L_{2}$ norm of output weights. We improve general MOPSO algorithm by adding chaotic mapping to promote the ergodicity and randomness of new solutions. In addition, a new strategy is proposed to select $pbest$ and $gbest$ to deal with the unbalance of multiple objectives. Experimental results on predicting two benchmark chaotic time series sustain the efficiency and effectiveness of our proposed algorithm. It has better generalization performance and smaller output weights than original ELM and other evolutionary ELMs.

Echo state network is a novel kind of recurrent neural networks, with a trainable linear readout layer and a large fixed recurrent connected hidden layer, which can be used to map the rich dynamics of complex real-world data sets. It has been extensively studied in time series prediction. However, there may be an ill-posed problem caused by the number of real-world training samples less than the size of the hidden layer. In this brief, a Laplacian echo state network (LAESN), is proposed to overcome the ill-posed problem and obtain low-dimensional output weights. First, an echo state network is used to map the multivariate time series into a large reservoir. Then, assuming that an unknown underlying manifold is inside the reservoir, we employ the Laplacian eigenmaps to estimate the manifold by constructing an adjacency graph associated with the reservoir states. Finally, the output weights are calculated by the low-dimensional manifold. In addition, some criteria of transient stability, local controllability, and local observability are given. Experimental results based on two real-world data sets substantiate the effectiveness and characteristics of the proposed LAESN model.

The just noticeable distortion (JND) model plays an important role in measuring the visual visibility for spread transform dither modulation (STDM) watermarking. However, the existing JND model characterizes the suprathreshold distortions with an equal saliency level. Visual saliency (VS) has been widely studied by psychologists and computer scientists during the last decade, where the distortions are more likely to be noticeable to any viewer. With this consideration, we proposed a novel STDM watermarking method for a monochrome image by exploiting a visual saliency-based JND model. In our proposed JND model, a simple VS model is employed as a feature to reflect the importance of a local region and compute the final JND map. Extensive experiments performed on the classic image databases demonstrate that the proposed watermarking scheme works better in terms of the robustness than other related methods.

For extreme learning machine (ELM), it is difficult to select an appropriate number of hidden layer nodes, which has a great influence on the prediction performance of ELM. Therefore, in order to overcome the influence of a large number of hidden layer nodes to prediction and enhance the prediction performance of ELM, an improved ELM is proposed based on orthogonal projections to latent structures (O-PLS) method. The improved ELM contains two steps. Firstly, the hidden layer of ELM is used to build the nonlinear mapping for input variables and get the output matrix H, where the number of bidden layer nodes can be random assigned. Then O-PLS is used to build the linear regression. Compared with least squares regression (LSR), O-PLS can effectively reduce the high dimensions, overcome the multicollinearity among H, which are caused by an improper number of hidden layer nodes. The simulation experiment results based on Lorenz time series and sunspots-runoff of Yellow river time series demonstrate that the improved ELM can effectively overcome the influence of hidden layer nodes to prediction performance and improve the stability performance and prediction performance of ELM.

An anatase TiO2(001) surface has shown great potential as an ideal and powerful photocatalyst due to its chemical stability, nontoxicity, and high reactivity. However, the fundamental atomic structure of the reconstructed anatase (001)-(1 × 4) surface is still under debate, which greatly impedes further exploration of its chemical activity. Herein, the anatase (001)-(1 × 4) surface reconstruction and the photocatalytic reactivity have been extensively studied using an effective surface structure searching method in combination with the first-principles calculations. Our study reveals that there exist two stable (1 × 4) reconstructed surfaces, i.e., the previously proposed "ad-molecule" (ADM) and oxidized ridge (OR) surface structures, and their simulated STM images are in good agreement with experimental observations. Moreover, we find that the ADM surface has superior photocatalytic reactivity to the OR surface and a small number of water can be dissociated at the terrace at one water monolayer coverage, which has never been found before. These findings can not only be applied to solve the experimental controversies about the atomic structure of the reconstructed anatase (001)-(1 × 4) surface but also provide a theoretical basis for exploring the intrinsic properties of the surface.