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In this work, a double distribution function-based lattice Boltzmann flux solver (LBFS) is proposed for simulating compressible viscous flows. This approach utilizes the double distribution function compressible lattice Boltzmann model and employs Chapman–Enskog expansion analysis to connect the lattice Boltzmann equation (LBE) with the Navier–Stokes (N–S) equations. Unlike conventional computational fluid dynamics methods that compute inviscid and viscous fluxes separately, the present method simultaneously evaluates both types of fluxes at the cell interface by locally reconstructing the solution of the LBE. Recognizing the significance of considering the non-equilibrium part of distribution functions for viscous flows, a straightforward method is introduced to calculate this component. This facilitates the derivation of computational expressions for macroscopic conservative variables and fluxes in the N–S equations. To validate the accuracy and stability of the present numerical scheme, various benchmark problems, including shock tube problem, Couette flow, lid-driven cavity flow, and flow around the NACA0012 airfoil, are tested. The obtained numerical results are compared with analytical solutions or existing reference data, confirming the capability of the proposed LBFS to deliver accurate and stable numerical results for compressible flows. Moreover, this method demonstrates effectiveness in handling viscous flow problems on non-uniform grids and with curved boundaries. [ABSTRACT FROM AUTHOR]

Fourier ptychography (FP) is an enabling imaging technique that produces high‐resolution complex‐valued images with extended field coverages. However, when FP images a phase object with any specific spatial frequency, the captured images contain only constant values, rendering the recovery of the corresponding linear phase ramp impossible. This challenge is not unique to FP but also affects other common microscopy techniques — a rather counterintuitive outcome given their widespread use in phase imaging. The underlying issue originates from the non‐uniform phase transfer characteristic inherent in microscope systems, which impedes the conversion of object wavefields into discernible intensity variations. To address this challenge, spatially‐coded Fourier ptychography (scFP) is presented for true quantitative phase imaging. In scFP, a flexible and detachable coded thin film is attached atop the image sensor in a regular FP setup. The spatial modulation of this thin film ensures a uniform phase response across the entire synthetic bandwidth. It improves reconstruction quality, corrects refractive index underestimation issues prevalent in conventional FP, and adds a new dimension of measurement diversity in spatial domain. The development of scFP is expected to catalyze new research directions and applications for phase imaging, emphasizing the need for true quantitative accuracy with uniform frequency response. [ABSTRACT FROM AUTHOR]

We have synthesized a quinone‐incorporated bistriarylamine donor‐acceptor‐donor (D–A–D) semiconductor 1 by B(C6F5)3 (BCF) catalyzed C−H/C−H cross coupling via radical ion pair intermediates. Coordination of Lewis acids BCF and Al(ORF)3 (RF=C(CF3)3) to the semiconductor 1 afforded diradical zwitterions 2 and 3 by integer electron transfer. Upon binding to Lewis acids, the LUMO energy of 1 is significantly lowered and the band gap of the semiconductor is significantly narrowed from 1.93 eV (1) to 1.01 eV (2) and 1.06 eV (3). 2 and 3 are rare near‐infrared (NIR) diradical dyes with broad absorption both centered around 1500 nm. By introducing a photo BCF generator, 2 can be generated by light‐dependent control. Furthermore, the integer electron transfer process can also be reversibly regulated via the addition of CH3CN. In addition, the temperature of 2 sharply increased and reached as high as 110 °C in 10 s upon the irradiation of near‐infrared‐II (NIR‐II) laser (1064 nm, 0.7 W cm−2), exhibiting a fast response to laser. It displays excellent photothermal stability with a photothermal (PT) conversion efficiency of 62.26 % and high‐quality PT imaging. [ABSTRACT FROM AUTHOR]

We have synthesized a quinone‐incorporated bistriarylamine donor‐acceptor‐donor (D–A–D) semiconductor 1 by B(C6F5)3 (BCF) catalyzed C−H/C−H cross coupling via radical ion pair intermediates. Coordination of Lewis acids BCF and Al(ORF)3 (RF=C(CF3)3) to the semiconductor 1 afforded diradical zwitterions 2 and 3 by integer electron transfer. Upon binding to Lewis acids, the LUMO energy of 1 is significantly lowered and the band gap of the semiconductor is significantly narrowed from 1.93 eV (1) to 1.01 eV (2) and 1.06 eV (3). 2 and 3 are rare near‐infrared (NIR) diradical dyes with broad absorption both centered around 1500 nm. By introducing a photo BCF generator, 2 can be generated by light‐dependent control. Furthermore, the integer electron transfer process can also be reversibly regulated via the addition of CH3CN. In addition, the temperature of 2 sharply increased and reached as high as 110 °C in 10 s upon the irradiation of near‐infrared‐II (NIR‐II) laser (1064 nm, 0.7 W cm−2), exhibiting a fast response to laser. It displays excellent photothermal stability with a photothermal (PT) conversion efficiency of 62.26 % and high‐quality PT imaging. [ABSTRACT FROM AUTHOR]

In this work, a virtual body-fitted grid is introduced into the velocity correction-based immersed boundary method (IBM) to simulate incompressible flows. The impact of the immersed boundary is indirectly transmitted to the flow field via a virtual body-fitted grid. In this method, the fractional step technique consisting of the predictor and the corrector is adopted. The prediction step is executed on the Eulerian mesh, and the correction step is done on the virtual grid to fulfill the no-slip boundary condition. After the correction step, the corrected velocity field on the virtual grid is then assigned to that on the Eulerian mesh to update the flow field. Being able to adjust the grid spacing flexibly, the virtual body-fitted grid alleviates the shortcomings of the conventional IBM that uses the smooth Dirac delta function to associate Lagrangian points with their surrounding Eulerian points. As a result, the present method is easy to apply to non-uniform Cartesian grids, which is inapplicable to the conventional IBM with the smooth Dirac delta function. Numerical experiments concerning flow past a circular cylinder and a NACA0012 airfoil demonstrate the advantages of the present method, i.e., fewer Lagrangian points are required to avoid the streamline penetration of boundary and the range of "diffuse interface" can be narrowed by reducing the normal grid spacing of the virtual body-fitted grid to improve numerical results on a coarse mesh. In addition, an accuracy assessment on the decaying vortex problem reveals that the present IBM has a second-order accuracy. [ABSTRACT FROM AUTHOR]

How to improve the computational efficiency of flow field simulations around irregular objects in near-continuum and continuum flow regimes has always been a challenge in the aerospace re-entry process. The discrete velocity method (DVM) is a commonly used algorithm for the discretized solutions of the Boltzmann-BGK model equation. However, the discretization of both physical and molecular velocity spaces in DVM can result in significant computational costs. This paper focuses on unlocking the key to accelerate the convergence in DVM calculations, thereby reducing the computational burden. Three versions of DVM are investigated: the semi-implicit DVM (DVM-I), fully implicit DVM (DVM-II), and fully implicit DVM with an inner iteration of the macroscopic governing equation (DVM-III). In order to achieve full implicit discretization of the collision term in the Boltzmann-BGK equation, it is necessary to solve the corresponding macroscopic governing equation in DVM-II and DVM-III. In DVM-III, an inner iterative process of the macroscopic governing equation is employed between two adjacent DVM steps, enabling a more accurate prediction of the equilibrium state for the full implicit discretization of the collision term. Fortunately, the computational cost of solving the macroscopic governing equation is significantly lower than that of the Boltzmann-BGK equation. This is primarily due to the smaller number of conservative variables in the macroscopic governing equation compared to the discrete velocity distribution functions in the Boltzmann-BGK equation. Our findings demonstrate that the fully implicit discretization of the collision term in the Boltzmann-BGK equation can accelerate DVM calculations by one order of magnitude in continuum and near-continuum flow regimes. Furthermore, the introduction of the inner iteration of the macroscopic governing equation provides an additional 1–2 orders of magnitude acceleration. Such advancements hold promise in providing a computational approach for simulating flows around irregular objects in near-space environments. [ABSTRACT FROM AUTHOR]

Multi-metric learning is important for improving performance of learners. For complex data, multi metric learning algorithms need intensive research. Moreover, the existing multi-metric learning methods may lead to the distance not being comparable. To solve these shortcomings and characterize better complexity data, we propose a novel multi-metric learning framework, where each class is divided into several clusters, and then a local metric and two concentric hypers-pheres are trained jointly in a cluster, such that the samples of the same cluster distribute within one hypersphere, and the classification margin are as large as possible simultaneously. This will leads to intra-class compactness and inter-class dispersion. During the test phase, the relative distance in learned metric space is designed to make classification decisions. A new example is classified to the class of its closest hyper-sphere center. This ensures that the comparison of distances is meaningful and avoids effectively the limitation of k-nearest neighbors (kNN) classifiers. Moreover,some important properties the proposed algorithm are analyzed theoretically. Further, an alternating iterative algorithm is developed to solve the problem. Numerical experiments are carried out on different scales and types datasets. Experiment results confirm the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Extraction and recovery of lithium from reserves play a critical role in the sustainable development of energy due to the explosive growth of the lithium‐battery market. However, the low efficiency of extraction and recovery seriously threatens the sustainability of lithium supply. In this contribution, we fabricate a novel mechanically robust fluorine‐rich hydrogel, showing highly efficient Li+ extraction from Li‐containing solutions. The hydrogel was facilely fabricated by simple one‐pot polymerization of supramolecular nanosheets of fluorinated monomers, acrylic acid and a small amount of chemical crosslinkers. The hydrogel exhibits a remarkable lithium adsorption capacity (Qm Li+=122.3 mg g−1) and can be reused. Moreover, it can exclusively extract lithium ions from multiple co‐existing metal ions. Notably, the separation of Li+/Na+ in actual wastewater is achieved with a surprising separation factor of 153.72. The detailed characterizations as well as calculation showed that the specific coordination of Li−F plays a central role for both of the striking recovery capability and selectivity for Li+. Furthermore, an artificial device was constructed, displaying high efficiency of extracting lithium in various complex actual lithium‐containing wastewater. This work provides a new and promising avenue for the efficient extraction and recovery of lithium resource from complex lithium‐containing solutions. [ABSTRACT FROM AUTHOR]

Extraction and recovery of lithium from reserves play a critical role in the sustainable development of energy due to the explosive growth of the lithium‐battery market. However, the low efficiency of extraction and recovery seriously threatens the sustainability of lithium supply. In this contribution, we fabricate a novel mechanically robust fluorine‐rich hydrogel, showing highly efficient Li+ extraction from Li‐containing solutions. The hydrogel was facilely fabricated by simple one‐pot polymerization of supramolecular nanosheets of fluorinated monomers, acrylic acid and a small amount of chemical crosslinkers. The hydrogel exhibits a remarkable lithium adsorption capacity (Qm Li+=122.3 mg g−1) and can be reused. Moreover, it can exclusively extract lithium ions from multiple co‐existing metal ions. Notably, the separation of Li+/Na+ in actual wastewater is achieved with a surprising separation factor of 153.72. The detailed characterizations as well as calculation showed that the specific coordination of Li−F plays a central role for both of the striking recovery capability and selectivity for Li+. Furthermore, an artificial device was constructed, displaying high efficiency of extracting lithium in various complex actual lithium‐containing wastewater. This work provides a new and promising avenue for the efficient extraction and recovery of lithium resource from complex lithium‐containing solutions. [ABSTRACT FROM AUTHOR]

In this work, the inverse distance weighting (IDW) interpolation is introduced into the implicit velocity correction-based immersed boundary method (IBM) for simulation of incompressible flows. In the original implicit velocity correction-based IBM, the solid body must be immersed in a uniform mesh region due to the use of the smooth Dirac delta function, which is utilized to associate Lagrangian points with their surrounding Eulerian points and only works with uniform meshes. The IDW method has the advantage that the interpolation range can be set flexibly. The introduction of the IDW interpolation can extend the application of the IBM to non-uniform meshes while reducing the number of Lagrangian points. The numerical test by the decaying vortex problem proves that the IDW interpolation does not significantly affect the overall accuracy of the IBM. In addition, numerical experiments for the flows around a circular cylinder and a NACA0012 airfoil demonstrate the advantages of the proposed method, including allowing fewer Lagrangian points while ensuring no streamline penetration to the solid body, as well as its adaptability to non-uniform meshes which can improve the computational efficiency due to the use of fewer mesh points. Finally, the simulation of the flow past a stationary sphere illustrates that the proposed method can effectively simulate the three-dimensional flow. [ABSTRACT FROM AUTHOR]

Distance metric learning has been a promising technology to improve the performance of algorithms related to distance metrics. The existing distance metric learning methods are either based on the class center or the nearest neighbor relationship. In this work, we propose a new distance metric learning method based on the class center and nearest neighbor relationship (DMLCN). Specifically, when centers of different classes overlap, DMLCN first splits each class into several clusters and uses one center to represent one cluster. Then, a distance metric is learned such that each example is close to the corresponding cluster center and the nearest neighbor relationship is kept for each receptive field. Therefore, while characterizing the local structure of data, the proposed method leads to intra-class compactness and inter-class dispersion simultaneously. Further, to better process complex data, we introduce multiple metrics into DMLCN (MMLCN) by learning a local metric for each center. Following that, a new classification decision rule is designed based on the proposed methods. Moreover, we develop an iterative algorithm to optimize the proposed methods. The convergence and complexity are analyzed theoretically. Experiments on different types of data sets including artificial data sets, benchmark data sets and noise data sets show the feasibility and effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Metric learning has attracted significant attention due to its high effectiveness and efficiency for pattern recognition task. Traditional supervised metric learning algorithms attempt to seek a global distance metric with labeled samples. When data are represented with multimodal and only limited supervision information is available, these approaches are insufficient to obtain satisfactory results. In this paper, we develop a robust semi-supervised multi-metric learning method (RSMM) to improve classification performance. The proposed RSMM learns multiple local metrics and a background metric instead of a single global metric. Specifically, we divide the metric space into influential regions and background region, and then regulate the effectiveness of each local metric to be within the related regions. Simultaneously, a geometrically interpretable, symmetric distance is defined with local metrics and background metric. Based on the resultant learning bounds, we obtain the regularization term to improve the classifier's generalization ability. Moreover, the manifold regularization term is introduced to preserve the supervision information as well as geometry structure. The substantial unlabeled samples may cause potential threats and large uncertainties, so the logarithmic loss function is utilized to enhance the robustness. An efficient gradient descent algorithm is exploited to solve the non-convex challenging problem. To further understand the proposed algorithm, we theoretically derive its robustness and generalization error bounds. Finally, numerical experiments on UCI datasets and image datasets demonstrate the feasibility and validity of the RSMM. [ABSTRACT FROM AUTHOR]

Finding efficient and versatile catalysts that can both produce clean energy H 2 and treat wastewater is an important matter to solve energy shortages and wastewater pollution. Herein, a feather-like NiCoP supported on NF was synthesized via the two-step hydrothermal-phosphorization process. NiCoP/NF requires only overpotentials of 44 and 203 mV to reach 10 mA cm−2 for HER and OER in 1 M KOH, respectively. Besides, NiCoP/NF requires only 1.13 V (vs RHE) to achieve 10 mA cm−2 in 1 M KOH containing 0.33 M urea. DFT calculation shows that NiCoP exhibits enhanced DOS in the Fermi level attachment, which promotes charge transfer. Subsequently, the trifunctional NiCoP/NF, for overall water splitting, requires a lower potential of 1.48 V to gain 10 mA cm−2 in 1 M KOH. For urea electrolysis, NiCoP/NF requires just 1.36 V to drive 10 mA cm−2 in 1 M KOH with 0.33 M urea. This work provides extraordinary insights into electrolytic hydrogen production and wastewater treatment through simple preparative methods. The performance of the prepared catalyst is at a high level in non-noble metal. [ABSTRACT FROM AUTHOR]

Urgent start peritoneal dialysis (USPD) is an effective therapeutic method for end-stage renal disease (ESRD). However, whether it is safe to initiate peritoneal dialysis (PD) within 24 h unclear. We examined the short-term outcomes of a break-in period (BI) of 24 h for patients undergoing USPD. This real-world, multicenter, retrospective cohort study evaluated USPD patients from five centers from January 2013 to August 2020. Patients were divided into BI ≤ 24 h or BI > 24 h groups. The Primary outcomes included incidence of mechanical and infectious complications. The secondary outcome was technique failure. Moreover, we presented a subgroup analysis for patients who did not receive temporary hemodialysis (HD). A total of 871 USPD patients were included: 470 in the BI ≤ 24 h and 401 in the BI > 24 h groups. Mechanical and infectious complications did not differ between the two groups across the follow-up timepoints (2 weeks, 1 month, 3 months, and 6 months) (p > 0.05). Multiple logistic regression analysis revealed that BI ≤ 24 h was not an independent risk factor for mechanical complications, catheter migration, or infectious complications (p > 0.05). A BI ≤ 24 h was not an independent significant risk factor for technique failure by multivariate Cox regression analysis (p > 0.05). The subgroup analysis of patients who did not receive temporary HD returned the same results. Initiating PD within 24 h of catheter insertion was not associated with increased mechanical complications, infectious complications, or technique failures. [ABSTRACT FROM AUTHOR]

Conventional immersed boundary methods (IBMs) have greatly simplified the boundary condition treatment by interpreting boundaries as forces in the source terms of governing equations. In conventional IBMs, uniform meshes of very high resolution must be applied near the immersed boundary to treat the solid–fluid interface. However, this can induce a high computational cost for simulating flows with large moving boundary domains, where everywhere along the trajectory of the moving object must be refined isotropically. In the worst scenario, a global refinement is required when the object is moving arbitrarily in the entire computational domain. In this work, an overset boundary condition-enforced immersed boundary method (overset BC-enforced IBM) is proposed to simulate incompressible flows with large moving boundary domains efficiently. In the proposed overset BC-enforced IBM, a locally refined uniform mesh is applied and fixed on the moving object to account for the local motions, e.g., the rotation and deformation of the object, while the global motion of the object is handled by embedding the locally refined mesh in a coarser background mesh. Both the local mesh and the global background mesh can be generated automatically using the Cartesian approach to avoid the cumbersome boundary treatment. Since the mesh refinement is local, considerable computational savings can be achieved. The overset BC-enforced IBM is combined with the lattice Boltzmann flux solver to simulate various fluid–structure interaction problems with rigid and deformable boundaries. [ABSTRACT FROM AUTHOR]

In recent years, deep-learning-based hyperspectral image (HSI) classification networks have become one of the most dominant implementations in HSI classification tasks. Among these networks, convolutional neural networks (CNNs) and attention-based networks have prevailed over other HSI classification networks. While convolutional neural networks with perceptual fields can effectively extract local features in the spatial dimension of HSI, they are poor at capturing the global and sequential features of spectral–spatial information; networks based on attention mechanisms, for example, Transformer, usually have better ability to capture global features, but are relatively weak in discriminating local features. This paper proposes a fusion network of convolution and Transformer for HSI classification, known as FusionNet, in which convolution and Transformer are fused in both serial and parallel mechanisms to achieve the full utilization of HSI features. Experimental results demonstrate that the proposed network has superior classification results compared to previous similar networks, and performs relatively well even on a small amount of training data. [ABSTRACT FROM AUTHOR]

The development of bifunctional electrocatalysts with efficient oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) is still a key challenge at the current stage. Herein, FeNi LDH/V2CTx/nickel foam (NF) self-supported bifunctional electrode was prepared via deposition of FeNi LDH on V2CTx/NF substrate by hydrothermal method. Strong interfacial interaction between V2CTx/NF and FeNi LDH effectively prevented the aggregation of FeNi LDH, thus exposing more catalytic active sites, which improved electrical conductivity of the nanohybrids and structural stability. The results indicated that the prepared FeNi LDH/V2CTx/NF required 222 mV and 151 mV overpotential for OER and HER in 1 M KOH to provide 10 mA cm−2, respectively. Besides, the FeNi LDH/V2CTx/NF electrocatalysts were applied to overall water splitting, which achieved a current density of 10 mA cm−2 at 1.74 V. This work provides ideas for improving the electrocatalytic performance of electrocatalysts through simple synthesis strategies, structural adjustment, use of conductive substrates and formation of hierarchical structures. [ABSTRACT FROM AUTHOR]

Microalgal technology holds great promise for both low C/N wastewater treatment and resource recovery simultaneously. Nevertheless, the advancement of microalgal technology is hindered by its reduced nitrogen removal efficiency in low C/N ratio wastewater. In this work, microalgae and waste oyster shells were combined to achieve a total inorganic nitrogen removal efficiency of 93.85% at a rate of 2.05 mg L−1 h−1 in low C/N wastewater. Notably, over four cycles of oyster shell reuse, the reactor achieved an average 85% ammonia nitrogen removal extent, with a wastewater treatment cost of only $0.092/ton. Moreover, microbial community analysis during the reuse of oyster shells revealed the critical importance of timely replacement in inhibiting the growth of non-functional bacteria (Poterioochromonas_malhamensi). The work demonstrated that the oyster shell - microalgae system provides a time- and cost-saving, environmental approach for the resourceful treatment of harsh low C/N wastewater. [Display omitted] • The removal rate of TIN was 2.05 mg L−1·h−1 in oyster shell - microalgae system. • The average NH 4 +-N removal rate reached 85% when oyster shells were reused. • Replacing oyster shells every 3 cycles prevents non-functional bacteria growth. [ABSTRACT FROM AUTHOR]

Time discrimination methods of pulse signals serve as the foundation for ranging, identification, and precision in pulse Lidar. Therefore, employing a judicious time discrimination method is pivotal in enhancing Lidar overall performance. This study initially delves into the theoretical distinctions among various time discrimination methods, including constant fraction timing, waveform centroid, dual-threshold waveform, and amplitude with rising edge compensation timing. It meticulously analyzes and compares these diverse methods for time discrimination. Subsequently, employing these methodologies to process Lidar echo signals to contrast their time measurement accuracy. The conclusions will provide valuable guidance for the design and practical application of pulse-type Lidar systems. [ABSTRACT FROM AUTHOR]

In the assessment of the tool wear state for the spiral edge of milling cutter based on machine vision, the traditional assessment criterion is often inaccurate due to the problem of missing of information, especially near the tip area. In order to deal with this problem, different lighting-condition settings and additional non-image information compensation techniques are needed. In view of this, an integration tool wear detection method combined line laser edge detection and machine vision is proposed. Subsequently, the combined data acquirement experimental system is designed and built, which can simultaneously acquire two types of parameters: diameter and images under the same detection condition. Then, based on the above information, a multi-dimension series assessment criterion is proposed consisting of three dimensions index: The one-dimensional assessment index gives the average wear value of the spiral side, which is mainly used as data ordination and characterizes the rule of tool wear degradation; the two-dimensional assessment index describes the contour of the wear region and calculates the area value, which can predict the change of the tool wear stage more precisely; and the three-dimensional assessment index gives the 3D morphology by adding depth information within the wear region and quantifies the volume of the worn-off part, which provides clues for early warning of the deterioration caused by the tool worn. Experiments proved that the multi-dimension series assessment criterion is helpful in reflecting the trend of tool life and giving a more accurate assessment of the tool wear state for the spiral edge of a milling cutter. [ABSTRACT FROM AUTHOR]

Metric learning has attracted a lot of interest in classification tasks due to its efficient performance. Most traditional metric learning methods are based on k-nearest neighbors (kNN) classifiers to make decisions, while the choice k affects the generalization. In this work, we propose an end-to-end metric learning framework. Specifically, a new linear metric learning (LMML) is first proposed to jointly learn adaptive metrics and the optimal classification hyperplanes, where dissimilar samples are separated by maximizing classification margin. Then a nonlinear metric learning model (called RLMML) is developed based on a bound nonlinear kernel function to extend LMML. The non-convexity of the proposed models makes them difficult to optimize. The half-quadratic optimization algorithms are developed to solve iteratively the problems, by which the optimal classification hyperplane and adaptive metric are alternatively optimized. Moreover, the resulting algorithms are proved to be convergent theoretically. Numerical experiments on different types of data sets show the effectiveness of the proposed algorithms. Finally, the Wilcoxon test shows also the feasibility and effectiveness of the proposed models. [ABSTRACT FROM AUTHOR]

• The implicit LBFS is proposed aiming to efficiently simulate both steady and unsteady flows. • The proposed solver works in the framework of finite volume method on both structured and unstructured meshes. • With a local compressible lattice Boltzmann model, the implicit LBFS is able to simulate subsonic and supersonic flows. The lattice Boltzmann flux solver (LBFS) is a novel Boltzmann-typed solver under the finite volume framework, which has many advantages over traditional lattice Boltzmann equation (LBE) solvers. However, the existing versions of LBFS are explicit in temporal discretization. Therefore, they are of low computational efficiency and are mostly applied to the simulation of flows around simple geometries. In this paper, an implicit lattice Boltzmann flux solver for compressible flows is proposed, which can efficiently march in time with a large CFL number, and therefore facilitates the simulation of flows around complex geometries in practical engineering applications. Aiming to obtain the time-accurate results of unsteady flows, the implicit LBFS is further combined with the dual time-stepping technique to efficiently simulate unsteady flows, in which a sub-iteration is applied on the pseudo time level. In the sub-iteration, the Jacobian matrix is preconditioned by the lower-upper symmetric Gauss-Seidel (LUSGS) method and solved by the generalized minimal residual method (GMRES). The accuracy and efficiency of the proposed solver are demonstrated by various simulations in a wide Mach number regime. To further demonstrate its performance in practical engineering applications, the proposed solver is also used to simulate the flow over complex aircraft models. [ABSTRACT FROM AUTHOR]

Transition metal sulfides have demonstrated excellent heavy metal detoxification in previous studies. However, the activity of their S-functional sites is usually not maximized because they are not precisely regulated. Herein, we report a new short-term cathodic tuned CuS crystals (C-tuned CuS) distortion strategy, ultrafast detoxicating over 95% Cr(VI) in 1 min，its detoxification rate was elevated by five orders of magnitude times compared to the untuned one. The high Cr(VI) detoxification performance is mainly attributed to the layered sulfur atoms tuning within series of cathodic potential shown by In-situ Raman. Besides, the four-tuning stages of C-tuned CuS lattice distortion based on the variation of Raman peak height and redshift changes was investigated by density functional theory (DFT) calculations, showing that the S-S bonds was stretched under cathodic tuning, extending from 2.106 to 2.345 Å. The stretched S-S bonds with enhanced polarity and electron activity prevailed the formation of transient Cr-S bonds with Cr(VI), favoring the direct electron transfer without O mediation, thus reducing the Cr(VI) reduction energy barrier. Cathodic tuning CuS crystal distortion, a "Plug and Play" strategy, show a new electron-level microscopic insights into wastewater reduction techniques development. [Display omitted] • Ultrashort-time cathodic electrical stimulation activates strong S-S activity in CuS. • The existence of four stages of S-S bond activation stretching is verified. • Two-electron-activated S-S bonds have strong reducing activity toward Cr(VI). • Activated S-S bonds don't need O as an intermediate for electron transfer thus reducing energy barriers. [ABSTRACT FROM AUTHOR]

Least squares twin support vector machine (LSTSVM) is an effective and efficient learning algorithm for pattern classification. However, the distance in LSTSVM is measured by squared L 2 -norm metric that may magnify the influence of outliers. In this paper, a novel robust least squares twin support vector machine framework is proposed for binary classification, termed as C L 2 , p -LSTSVM, which utilizes capped L 2 , p -norm distance metric to reduce the influence of noise and outliers. The goal of C L 2 , p -LSTSVM is to minimize the capped L 2 , p -norm intra-class distance dispersion, and eliminate the influence of outliers during training process, where the value of the metric is controlled by the capped parameter, which can ensure better robustness. The proposed metric includes and extends the traditional metrics by setting appropriate values of p and capped parameter. This strategy not only retains the advantages of LSTSVM, but also improves the robustness in solving a binary classification problem with outliers. However, the nonconvexity of metric makes it difficult to optimize. We design an effective iterative algorithm to solve the C L 2 , p -LSTSVM. In each iteration, two systems of linear equations are solved. Simultaneously, we present some insightful analyses on the computational complexity and convergence of algorithm. Moreover, we extend the C L 2 , p -LSTSVM to nonlinear classifier and semi-supervised classification. Experiments are conducted on artificial datasets, UCI benchmark datasets, and image datasets to evaluate our method. Under different noise settings and different evaluation criteria, the experiment results show that the C L 2 , p -LSTSVM has better robustness than state-of-the-art approaches in most cases, which demonstrates the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Highlights: This review systematically summarizes the source of electricity, the key choice of catalyst, and the potentiality of electrolyte for prospective hydrogen generation. Each section provides comprehensive overview, detailed comparison and obvious advantages in these system configurations. The problems of hydrogen generation from electrolytic water splitting and directions of next-generation green hydrogen in the future are discussed and outlooked. Direct regeneration method has been widely concerned by researchers in the field of battery recycling because of its advantages of in situ regeneration, short process and less pollutant emission. In this review, we firstly analyze the primary causes for the failure of three representative battery cathodes (lithium iron phosphate, layered lithium transition metal oxide and lithium cobalt oxide), targeting at illustrating their underlying regeneration mechanism and applicability. Efficient stripping of material from the collector to obtain pure cathode material has become a first challenge in recycling, for which we report several pretreatment methods currently available for subsequent regeneration processes. We review and discuss emphatically the research progress of five direct regeneration methods, including solid-state sintering, hydrothermal, eutectic molten salt, electrochemical and chemical lithiation methods. Finally, the application of direct regeneration technology in production practice is introduced, the problems exposed at the early stage of the industrialization of direct regeneration technology are revealed, and the prospect of future large-scale commercial production is proposed. It is hoped that this review will give readers a comprehensive and basic understanding of direct regeneration methods for used lithium-ion batteries and promote the industrial application of direct regeneration technology. [ABSTRACT FROM AUTHOR]

Objective: To compare the accuracy, efficiency, and consistency between experienced and less‐experienced professionals using intelligent navigation echocardiography. Methods: In this prospective study, we enrolled 93 second‐ and third‐trimester fetuses with conotruncal defects (CTD) from July 2017 to February 2018. One or more spatiotemporal image correlation volume data sets were collected per case. The fetuses with CTD were diagnosed by the following two groups of professionals (n = 20 in each) with different experience levels using intelligent navigation echocardiography and two‐dimensional ultrasound: group A with 15 years of experience and group B with 1 year of experience. The diagnostic consistency and accuracy of the technologies between the two groups were analyzed. Results: Satisfactory consistency was noted in the two groups (group A, τ = 0.855, P < 0.05, and group B, τ = 0.821, P < 0.05), and no significant difference in accuracy (χ2 = 3.218, P > 0.05) in using intelligent navigation echocardiography was reported between the two groups. However, there a significant difference in accuracy (χ2 = 0.021, P < 0.05) when using two‐dimensional ultrasound was observed between the two groups. Conclusion: Intelligent navigation echocardiography was found to be efficient and accurate for the diagnosis of CTD and good consistency existed in the experienced and less‐experienced professionals. Intelligent navigation echocardiography was feasible and accurate for the diagnosis of fetal conotruncal defects. [ABSTRACT FROM AUTHOR]

For K ⊂ C a compact subset and μ a positive finite Borel measure supported on K , let R ∞ (K , μ) be the weak-star closure in L ∞ (μ) of rational functions with poles off K. We show that if R ∞ (K , μ) has no non-trivial L ∞ summands and f ∈ R ∞ (K , μ) , then f is invertible in R ∞ (K , μ) if and only if Chaumat's map for K and μ applied to f is bounded away from zero on the envelope with respect to K and μ. The result proves the conjecture ⋄ posed by J. Dudziak [6] in 1984. [ABSTRACT FROM AUTHOR]

We propose a new distribution-free Bayes optimal classifier, called the twin minimax probability machine (TWMPM), which combines the benefits of both minimax probability machine(MPM) and twin support vector machine (TWSVM). TWMPM tries to construct two nonparallel hyperplanes such that each hyperplane separates one class samples with maximal probability, and is distant from the other class samples simultaneously. Moreover, the proposed TWMPM can control the misclassification error of samples in a worst-case setting by minimizing the upper bound on misclassification probability. An efficient algorithm for TWMPM is first proposed, which transforms TWMPM into concave fractional programming by applying multivariate Chebyshev inequality. Then the proposed TWMPM is reformulated as a pair of convex quadric programming (QP) by proper mathematical transformations. This guarantees TWMPM to have global optimal solution and be solved simply and effectively. In addition, we develop also an iterative algorithm for the proposed TWMPM. By comparing the two proposed algorithms theoretically, it is easy to know that the convex quadric programming algorithm is with lower computation burden than iterative algorithm for the TWMPM. A linear TWMPM version is first built, and then we show how to exploit mercer kernel to obtain nonlinear TWMPM version. The computation complexity for QP algorithm of TWMPM is in the same order as the traditional twin support vector machine (TWSVM). Experiments are carried out on three databases: UCI benchmark database, a practical application database and an artificial database. With low computation complexity and fewer parameters, experiments show the feasibility and effectiveness of the proposed TWMPM and its QP algorithm. • Propose a twin minimax probability machine (TWMPM) classification framework. • TWMPM has advantages of both MPM and TWSVM. • With maximal probability, TWMPM separates two-class samples. • Reformulate TWMPM as concave maximization problems with global optimal solutions. • TWMPM is with low computation complexity and few parameters. [ABSTRACT FROM AUTHOR]

• A robust twin bounded SVM (RTBSVM) is proposed. • The least squares version (FRTBSVM) of RTBSVM is proposed. • Two effective algorithms are derived. • The convergence of algorithms are proved and computational complexity is analyzed. • Experiments show that RTBSVM and FRTBSVM are feasibility and effectiveness. In this paper, to improve the performance of capped L 1 -norm twin support vector machine (CTSVM), we first propose a new robust twin bounded support vector machine (RTBSVM) by introducing the regularization term. The significant advantage of our RTBSVM over CTSVM is that the structural risk minimization principle is implemented. This embodies the marrow of statistical learning theory, so this modification can improve the performance of classification. Furthermore, to accelerate the computation of RTBSVM and simultaneously inherit the merit of robustness, we construct a least squares version of RTBSVM (called RTBSVM). This formulation leads to a simple and fast algorithm for binary classifiers by solving just two systems of linear equations. Finally, we derive two simple and effective iterative optimization algorithms for solving RTBSVM and FRTBSVM, respectively. Simultaneously, we theoretically rigorously analyze and prove the computational complexity, local optimality and convergence of the algorithms. Experimental results on one synthetic dataset and nine UCI datasets demonstrate that our methods are competitive with other methods. Additionally, the FRTBSVM is directly applied to recognize the purity of hybrid maize seeds using near-infrared spectral data. Experiments show that our method achieves better performance than the traditional methods in most spectral regions. [ABSTRACT FROM AUTHOR]

The classical principal component analysis (PCA) is not sparse enough since it is based on the L2-norm that is also prone to be adversely affected by the presence of outliers and noises. In order to address the problem, a sparse robust PCA framework is proposed based on the min of zero-norm regularization and the max of Lp-norm (0 < p ≤ 2) PCA. Furthermore, we developed a continuous optimization method, DC (difference of convex functions) programming algorithm (DCA), to solve the proposed problem. The resulting algorithm (called DC-LpZSPCA) is convergent linearly. In addition, when choosing different p values, the model can keep robust and is applicable to different data types. Numerical simulations are simulated in artificial data sets and Yale face data sets. Experiment results show that the proposed method can maintain good sparsity and anti-outlier ability. [ABSTRACT FROM AUTHOR]

Symmetric loss functions are widely used in regression algorithms to focus on estimating the means. Huber loss, a symmetric smooth loss function, has been proved that it can be optimized with high efficiency and certain robustness. However, mean estimators may be poor when the noise distribution is asymmetric (even outliers caused heavy-tailed distribution noise) and estimators beyond the means are necessary. Under the circumstances, quantile regression is a natural choice which estimates quantiles instead of means through asymmetric loss functions. In this paper, an asymmetric Huber loss function is proposed to implement different penalty for overestimation and underestimation so as to deal with more general noise. Moreover, a smooth truncated version of the proposed loss is introduced to enhance stronger robustness to outliers. Concave-convex procedure is developed in the primal space with the proof of convergence to handle the non-convexity of the involved truncated objective. Experiments are carried out on both artificial and benchmark datasets and robustness of the proposed methods are verified. [ABSTRACT FROM AUTHOR]

Exploiting high performance electrocatalysts is crucial for the effective electrooxidation of methanol, although some barriers exist. Herein, we develop a hybrid support composed of graphitic carbon nitride (g-C 3 N 4) and reduced graphene oxide (rGO) synergistically anchoring sufficient ultrafine palladium (Pd) nanoparticles via a simple one-step electrodeposition technique. The morphology and structure were characterized by scanning/transmission electron microscopy, X-ray diffraction, Raman spectroscopy and X-ray photoelectron spectroscopy, which confirmed that the Pd nanoparticles were massively and uniformly dispersed on the support of g-C 3 N 4 @rGO with a the average particle size of 5.87 nm, deriving from the nitrogen in g-C 3 N 4 contributing to the electron transport highway on the rGO nanosheet layer surface. Furthermore, electrochemical results suggested that the Pd/g-C 3 N 4 @rGO showed a high electrocatalytic efficiency for methanol oxidation with a high current density reached 0.131 mA cm−2. Based on a novel approach to the g-C 3 N 4 @rGO hybrid nanostructure, this work offers a promising method for the design and synthesis for the superior performance methanol electrocatalyst. Reduced graphene oxide hybridized graphitic carbon nitride anchoring Pd nanoparticles hybrid catalyst is prepared via a simple electrodeposition technique. The catalyst exhibits high electrocatalytic activity and long-term durability for methanol electrooxidation reaction. Image 1 • RGO hybridized g-C 3 N 4 anchoring ultrafine Pd is reported. • The hybrid electrocatalyst is achieved via a one-step cyclic voltammetry. • The current density of Pd/g-C 3 N 4 @rGO electrode was 4.5 times that of Pd@rGO. • The electrodeposition technique is rapid, simple and green. [ABSTRACT FROM AUTHOR]

Uncertain or missing data may occur in many practical applications. A principled strategy for handling this problem would therefore be very useful. We consider two-class and multi-class classification problems where the mean and covariance of each class are assumed to be known. With simple structure, fast speed and good performance, extreme learning machine (ELM) has been an important technology in machine learning. In this work, from the viewpoint of probability, we present a robust ELM framework (RELM) for missing data classification. Applying the Chebyshev–Cantelli inequality, the proposed RELM is reformulated as a second-order cone programming with global optimal solution. The proposed RELM only relates to the second moments of input samples and makes no assumption about the data probability distribution. Expectation maximization algorithm is used to fill in missing values and then obtain complete data. Numerical experiments are simulated in various datasets from UCI database and a practical application database. Experimental results show that the proposed method can achieve better performance than traditional methods. These results illustrate the feasibility and effectiveness of the proposed method for missing data classification. [ABSTRACT FROM AUTHOR]

• A green synthesis approach for anti-fouling modifications. • Molecular drilling effect by zwitterionic copolymers enhances water permeability. • Pure water permeability 10.5 ± 0.5 L m−2 h−1 bar−1, NaCl rejection of 98.1 ± 0.2%. • Fouling resistant to various foulants and realistic feed. This paper reports a fast, cost-effective and environmental-friendly method to engineer the membrane structure, and simultaneously enhance the permeance and fouling resistance in reverse osmosis (RO). By free radical polymerization in water, copolymers containing fouling-resistant zwitterionic and hydroxyl-based anchoring groups are synthesized and quickly bonded to the polyamide membrane surface. The copolymer also functions as the molecular engine to reduce the thickness of the polyamide layer and drill nanopores, thereby re-shaping its 3D structure for higher permeability. It is revealed that by design of the size and functionality of the copolymer, the water permeability and salt rejection may be simultaneously increased. Our membrane demonstrates a pure water permeability of 10.5 L m−2 h−1 bar−1, NaCl rejection of 98.1%, and superior fouling resistance towards various foulants and realistic feed. This paper provides a new perspective for the development of highly permeable, fouling-resistance membranes not only for RO, but also nanofiltration. [ABSTRACT FROM AUTHOR]

Abstract Let K be a compact subset of the complex plane C. Let P (K) and R (K) be the closures in C (K) of analytic polynomials and rational functions with poles off K , respectively. Let A (K) ⊂ C (K) be the algebra of functions that are analytic in the interior of K. For 1 ≤ t < ∞ , let P t (1 , ϕ 1 ,... , ϕ N , K) be the closure of P (K) + P (K) ϕ 1 +... + P (K) ϕ N in L t (d A | K) , where d A | K is the area measure restricted to K and ϕ 1 ,... , ϕ N ∈ L t (d A | K). Let H P (ϕ 1 ,... , ϕ N , K) be the closure of P (K) ϕ 1 +... + P (K) ϕ N + R (K) in C (K) , where ϕ 1 ,... , ϕ N ∈ C (K). In this paper, we prove if R (K) ≠ C (K) , then there exists an analytic bounded point evaluation for both P t (1 , ϕ 1 ,... , ϕ N , K) and H P (ϕ 1 ,... , ϕ N , K) for certain smooth functions ϕ 1 ,... , ϕ N , in particular, for z ¯ , z ¯ 2 ,... , z ¯ N. We show that A (K) ⊂ H P (z ¯ , z ¯ 2 ,... , z ¯ N , K) if and only if R (K) = A (K). In particular, C (K) ≠ H P (z ¯ , z ¯ 2 ,... , z ¯ N , K) unless R (K) = C (K). We also give an example of K showing the results are not valid if we replace z ¯ n by certain ϕ n , that is, there exist K and a function ϕ ∈ A (K) such that R (K) ≠ A (K) , but A (K) = H P (ϕ , K). [ABSTRACT FROM AUTHOR]

Abstract Underwater imaging is attractive but challenging. Images could be severely degraded by the particles in turbid water because of backscatter generation and signal light attenuation. In this paper, we focus on the scheme of underwater imaging and study the methods of visibility enhancement of turbid underwater images based on polarimetric imaging utilizing active unpolarized illumination. Compared with traditional polarimetric imaging using linearly polarized illumination, using unpolarized illumination ensures the polarization effect of the signal light could be neglected, no matter the depolarization degree of the object is high or low, which expands the application range of underwater polarimetric imaging and makes the underwater polarimetric imaging scheme more reliable and robust. Experimentally, the visibility and the contrast of underwater images are enhanced effectively. In addition, it is demonstrated that our method is applicable for objects of different materials and different imaging distances in turbid water. The contrast of underwater images could be promoted at least 100%, meaning that this kind of technique can be potentially used in many underwater environments. [ABSTRACT FROM AUTHOR]

In this work we propose a minimax probability extreme learning machine framework (MPME), which combines the benefits of minimax probability machine (MPM) with extreme learning machine (ELM). For binary classification problems, we illustrate that the proposed MPME can be interpreted geometrically by minimizing the maximum of Mahalanobis distances to the two classes. Then two variants of the MPME are presented based on the l 2 -norm loss and l 1 -norm loss functions (called LSEMPME and LADMPME) respectively. Without making specific assumption on the data distribution, the proposed methods can provide explicit upper-bounds for the generalization error, moreover the LSEMPME and LADMPME minimize empirical risk simultaneously. The decision hyperplanes of the proposed methods pass through the origin in ELM feature space with few decision variables. By using the multivariate Chebyshev–Cantelli inequality, all the proposed problems can be reformulated as second-order cone programming (SOCP) with global solutions. Furthermore, numerical experiments have been carried out on two databases that are drawn from UCI benchmark database and a practical application database. First, the proposed methods are evaluated for a practical application consisting on the analysis of licorice seeds using near-infrared spectral (NIR) data. Experiments in six different spectral regions illustrate that the proposed methods can improve generalization in most cases. Then the proposed methods are evaluated on benchmark datasets. In comparison with traditional methods including MPM, ELM and support vector machine (SVM), experiments show that the proposed methods achieve comparable results in generalization. With few decision variables, the proposed methods are easy to implement for nonlinear classification and to estimate a lower-bound on the prediction accuracy. • We propose a new minimax probability extreme learning machine (MPME). • We extend MPME based on 1-norm and 2-norm loss functions respectively. • All problems are posed as SOCP problems with global solutions. • MPME can provide an explicit lower bound on correct classification probability. • MPME has good interpretation geometrically in ELM feature space. [ABSTRACT FROM AUTHOR]

Abstract We propose a new homotopy loss, where practitioners can tune the parameter to derive different loss functions such as l 1 - n o r m loss, logarithmic loss, Geman–Reynolds loss, Geman–McClure loss and correntropy-based loss. Moreover, we illustrate that the proposed loss satisfies Fisher consistency, and we analyze the robustness of the proposed homotopy loss from different perspectives: M-estimation and adversarial perturbations. Then, we represent a new evaluation standard to measure robustness and demonstrate its upper bound to ensure the validity of this measure. Applied the proposed homotopy loss to least square support vector machine (LSSVM) and the extreme learning machine (ELM), two robust models are presented to enhance the robustness. Furthermore, re-weighted least square algorithm is used to solve the problems, and the resulting algorithms converge globally. In addition, the proposed methods are implemented on various datasets with different levels of noise. Compared with traditional methods, experiment results on real-world datasets show that the proposed methods have superior anti-interference ability to outliers in most cases. [ABSTRACT FROM AUTHOR]

C−H alkenylation of cyclic ethers (THF, 1,4‐dioxane) using the readily available nitroalkenes as the alkenylating reagents has been developed. It allows the rapid access to the α‐alkenyl ethers with high E‐selectivity. The previous inaccessible α‐dienyl ethers are successfully obtained. Acyclic ether can also participate in this alkenylation process. The mechanism study reveals that alkenylation proceeded through a proton coupled electron transfer (PCET) process with a de‐nitration. Photo mediator: An organic photocatalyst combined with suitable base allow the employment of readily available versatile nitroalkenes for alkenylation of ethers. Good Z/E selectivity was obtained via an efficient proton coupled electron transfer process. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Ultrafine Pd loaded Ni foam electrode is obtained by simple replacement method. • The Pd@Ni-foam exhibits an excellent electroreductive dechloridation performance. • Antibiotic toxicity was completely removed through Pd@Ni-foam cathode. • The synergistic effect between Pd and Ni was first proposed. Abstract Electrocatalytic hydrodechlorination is deemed as a promising environmental remediation technology for fast and effective detoxification of halogenated antibiotic pollutants. In this study, a novel nickel (Ni) foam electrode decorated by ultrafine palladium (Pd) nanoparticles (denoted as Pd@Ni-foam) was reported for electrocatalytic dechlorination of florfenicol (FLO, a typical antibiotic). The Pd@Ni-foam electrode was synthesized via a facile and simple spontaneous galvanic replacement of surface Ni atoms of Ni foam with Pd precursor. The obtained Pd@Ni-foam catalyst showed an excellent performance on electrochemical dechloridation of FLO. The dechloridation efficiency of FLO (20 mg L−1) reached 99.5% with the dechloridation rate as fast as 16.58 mg min−1 at cathode potential of –1.2 V (vs. saturated Ag/AgCl), which far prevailed over pure Ni foam (2.63 mg min−1) and commercial Pd/C (11.32 mg min−1). The reaction rate constant (0.044 min−1) of FLO dechloridation at Pd@Ni-foam electrode was 44 and 4.4 times higher than that of pure Ni-foam (0.001 min−1) and commercial Pd/C (0.010 min−1), respectively. The excellent electrocatalysis for FLO dechloridation of Pd@Ni-foam cathode is ascribed to the 3D self-supported Ni-foam frame for mass transfer enhancement and the formation of Pd-Ni micro-interfaces leading to the high utilization of atomic H*. Furthermore, the toxicity assessment showed that the antibacterial activity of FLO was complete elimination after electrocatalytic dechlorination process. [ABSTRACT FROM AUTHOR]

Abstract Exploring a Z-scheme heterojunction photocatalyst with efficient charge separation and outstanding redox ability is necessary but very challenging. Herein, a novel direct Z-scheme MoSe 2 decorating TiO 2 nanotube arrays photocatalyst (denoted as MoSe 2 @TNTs) was prepared by a facile in situ hydrothermal method. Few-layer MoSe 2 nanosheets distribute on the surface of the TNTs without shielding the tubular structure. Compared with individual TNTs and MoSe 2 , the as-prepared MoSe 2 @TNTs composite displays much better photocatalytic activity for eliminating pollutants (such as 4-nitrophenol and hexavalent chromium). Investigation results reveal that TNTs combined with appropriate amount of MoSe 2 as co-catalyst has direct Z-scheme construction instead of traditional type-II heterojunction, which can offer abundant photo-generated active species with high redox capacity. Graphical abstract Image 10 Highlights • Few-layer MoSe 2 nanosheets decorated TiO 2 NTs via a facile hydrothermal method. • MoSe 2 @TNTs exhibited high catalytic activity towards degradation of 4-NP and CrVI. • A direct Z-scheme mechanism was proposed for MoSe 2 @TNTs composites. [ABSTRACT FROM AUTHOR]

Highlights • A new robust loss function is designed for imbalanced data sets. • The proposed model combines this loss with SVM. • The robustness of model is analyzed in theory. • The Bayes optimal solution is derived. • Alternative iterative algorithm is designed to reduce algorithm complexity. Abstract Based on minimizing misclassification cost, a new robust loss function is designed in this paper to deal with the imbalanced classification problem under noise environment. It is nonconvex but maintains Fisher consistency. Applying the proposed loss function into support vector machine (SVM), a robust SVM framework is presented which results in a Bayes optimal classifier. However, nonconvexity makes the model difficult to optimize. We develop an alternative iterative algorithm to solve the proposed model. What's more, we analyze the robustness of the proposed model theoretically from a re-weighted SVM viewpoint and the obtained optimal solution is consistent with Bayesian optimal decision rule. Furthermore, numerical experiments are carried out on databases that are drawn from UCI Machine Learning Repository and a practical application. With two different types of noise environments, one with label noise and one with feature noise, experiment results show that on these two databases the proposed method achieves better generalization results compared to other SVM methods. [ABSTRACT FROM AUTHOR]

Primitive arc magmas in oceanic island arcs are probes of sub-arc magmatic processes and are crucial for understanding oceanic subduction. We report data for an Early Paleozoic oceanic arc volcanic complex in the Lajishan–Yongjing terrane, South Qilian Accretionary Belt (SQAB), Qilian Orogen, including zircon U–Pb dating and Hf–O isotopes, mineral and whole-rock geochemistry, and Sr–Nd isotope compositions. New zircon ages of ∼455–440 Ma constrain the timing of arc volcanism and the subduction of the Qilian Ocean. Based on petrography and bulk-rock composition, five lithological types have been identified: ankaramite; high-Mg basaltic andesite; high-Al andesite; boninite; sanukite. The volcanic sequence thus is one of the few island arcs where three types of near-primitive arc rocks including boninite, ankaramite and sanukite have been simultaneously produced. All these rocks have variably enriched Sr–Nd isotopic compositions, positive to slight negative zircon εHf(t) values and elevated zircon δ18O values. Boninites, ankaramites and sanukites are interpreted as contemporary near-primitive melts generated from different sources and conditions within an island arc setting. Boninites are characterized by low Ti and REE concentrations and high Cr# chrome spinel, and are interpreted as melts of refractory, Cpx-poor, spinel lherzolite or harzburgite at >25% partial melting. Anomalous zircon δ18O values of 6·57–7·61‰ and Sr–Nd mixing calculations suggest less than 2% incorporation of subducted oceanic sediments into the mantle source of the magmas. The ankaramites are characterized by low SiO2 and high MgO (Mg#), Cr, Ni and La/Yb ratios, and have similar isotopic ratios to tectonically adjacent ocean island basalt (OIB) lavas. The ankaramite lavas are likely to have derived from mantle sources similar to those of OIB; that is, pyroxenite-bearing garnet peridotite enriched in incompatible elements. High-Mg basaltic andesites and high-Al andesites may be derived from parental ankaramite magmas. Sr–Nd–Hf isotopic mixing modeling constrains the amount of silicic melt to ∼1–4% for ankaramite magma. Sanukites are of andesitic–dacitic composition with high Mg#, Cr and Ni, and enriched large ion lithophile elements and high La/Yb ratios. They are interpreted as having been generated by reaction of mantle peridotite with a silicic melt, itself derived from subducted sediments. Enriched Sr–Nd–Hf isotopic compositions constrain the amount of silicic melt to ∼10–15% for sanukite. Large compositional variations among the volcanic rocks from the same arc reflect heterogeneous mantle sources and variable degrees of mantle metasomatism by sediment-derived hydrous fluids or silicic melts, accompanied by secondary assimilation–fractional crystallization processes during magma ascent to the surface. The generation of the island arc volcanic sequence in the Lajishan–Yongjing Terrane is a response to the collision between the Lajishan–Yongjing Oceanic Plateau (recorded by the Lajishan–Yongjing Ophiolite) and the pre-existing trench–continental margin. Evolution from a continental margin in the North Qilian Accretionary Belt to an oceanic island arc in the SQAB records subduction advance and retreat in the history of the Qilian Ocean. [ABSTRACT FROM AUTHOR]

The simulation of Chinese pulse diagnosis via acquisition and qualitative description of temporal and spatial pulse signals is important for objective pulse diagnosis in traditional Chinese medicine. In order to simulate pulse diagnosis by fingers, we developed a novel pulse signal detection device based on the airbag-type flexible probe, in which the pulse-taking pressure was adjusted using lever principle. A contact film was fixed at the tip of the probe, and two cameras were used to synchronously taking the deformation image of the contact film. Grid-pattern lines were printed on the contact film. Image coordinates of the intersection points were detected using ridge line fitting method. Then, the 3D coordinates of the intersection points were calculated based on binocular vision measurement principle and the deformations of the contact film was reconstructed through surface fitting. Finally, the 3D pulse image series containing spatial and temporal information were acquired. Using the acquired 3D pulse images, the pulse conditions can be quantitatively described, indicating the 3D pulse images are an effective way of quantitatively describing pulse conditions. [ABSTRACT FROM AUTHOR]

Correntropy is a local similarity measure between two arbitrary variables, and it has been applied in a variety of learning algorithms to improve noise insensitivity. In this paper, based on the correntropy, a non-convex and bounded loss function is obtained which contains second and higher order moments of the classification margin. And the novel loss function is robust to noises and close to the 0–1 loss function. Then we introduce it into extreme learning machine (ELM), and propose a correntropy-based robust ELM framework for classification, trained by half quadratic optimization to cope with non-convexity of the algorithm. To evaluate robustness, feature noise and label noise are simulated to provide noisy environments. Experimental results on benchmark datasets demonstrate that the proposed algorithm is better than original algorithms and robust algorithms. Moreover, the superiority of proposed algorithm in noisy environment is more evident, which further proves its robustness to noises. [ABSTRACT FROM AUTHOR]

Ion imprinted polymer (IIP) technology is traditionally used to selectively recover heavy metal ions from wastewater, but the low adsorption capacity and high cost has restricted its large-scale practical application. Exploring a novel method with high selectivity and low cost to capture heavy metals is necessary but very challenging. Herein, we first report a highly selective and high-performance absorbent via free radical polymerization of thiolactone functionalized acrylamide (named as PAM-TL). The PAM-TL demonstrated much higher capacity (145.2 mg/g) for Ag(I), which is 5.8 times that of corresponding IIP (25.07 mg/g). Moreover, the distribution coefficient of PAM-TL (576.1) was 5.1 times that of the IIP material (113.8). Furthermore, PAM-TL absorbent was also equipped with superior stability and recyclability (outstanding adsorption performance after six cycles). The excellent performance was attributed to the abundant binding site density and the strong interaction between absorbent and Ag(I), which was determined by X-ray photoelectron spectroscopy and elemental analysis. This work provides a novel approach for highly effective and selective capturing heavy metal ions, which is of great significance to actual applications in wastewater treatment. [ABSTRACT FROM AUTHOR]

Summary: Drought stress conditions decrease maize growth and yield, and aggravate preharvest aflatoxin contamination. While several studies have been performed on mature kernels responding to drought stress, the metabolic profiles of developing kernels are not as well characterized, particularly in germplasm with contrasting resistance to both drought and mycotoxin contamination. Here, following screening for drought tolerance, a drought‐sensitive line, B73, and a drought‐tolerant line, Lo964, were selected and stressed beginning at 14 days after pollination. Developing kernels were sampled 7 and 14 days after drought induction (DAI) from both stressed and irrigated plants. Comparative biochemical and metabolomic analyses profiled 409 differentially accumulated metabolites. Multivariate statistics and pathway analyses showed that drought stress induced an accumulation of simple sugars and polyunsaturated fatty acids and a decrease in amines, polyamines and dipeptides in B73. Conversely, sphingolipid, sterol, phenylpropanoid and dipeptide metabolites accumulated in Lo964 under drought stress. Drought stress also resulted in the greater accumulation of reactive oxygen species (ROS) and aflatoxin in kernels of B73 in comparison with Lo964 implying a correlation in their production. Overall, field drought treatments disordered a cascade of normal metabolic programming during development of maize kernels and subsequently caused oxidative stress. The glutathione and urea cycles along with the metabolism of carbohydrates and lipids for osmoprotection, membrane maintenance and antioxidant protection were central among the drought stress responses observed in developing kernels. These results also provide novel targets to enhance host drought tolerance and disease resistance through the use of biotechnologies such as transgenics and genome editing. [ABSTRACT FROM AUTHOR]