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In the paper, a novel kernel recursive least-squares (KRLS) algorithm named random Fourier feature kernel recursive maximum mixture correntropy (RFF-RMMC) algorithm is proposed, which improves the prediction efficiency and robustness of the KRLS algorithm. Random Fourier feature (RFF) method as well as maximum mixture correntropy criterion (MMCC) are combined and applied into KRLS algorithm afterwards. Using RFF to approximate the kernel function in KRLS with a fixed cost can greatly reduce the computational complexity and simultaneously improve the prediction efficiency. In addition, the MMCC maintains the robustness like the maximum correntropy criterion (MCC). More importantly, it can enhance the accuracy of the similarity measurement between predicted and true values by more flexible parameter settings, and then make up for the loss of prediction accuracy caused by RFF to a certain extent. The performance of the RFF-RMMC algorithm for online time series prediction is verified by the simulation results based on three datasets.

Although ultrasound-induced reactive oxygen species (ROS) have believed to be primary intermediates for the realization of sonodynamic therapy and sonocatalytic reactions, the mechanism of ROS production under ultrasound has not been well clarified till now. Here we discovered that ultrasound cavitation can break up oxygen-containing groups on the surface of carbon dots (CDs) to generate ROS, giving rise to a novel mechanism different from as-reported those. The bond cleavage of oxygen-containing groups contribute transient oxygen free radicals (•O−) that involve in the subsequent formation of ROS by quickly reacting with hydrogen ions and water, thereby leading to a twice higher sonocatalytic activity of CDs than TiO2. Moreover, the sonosensitizer function of CDs can be tuned by pH values of the surrounding environment that determine the generation and evolution reaction of oxygen free radicals under ultrasound irradiation. This finding paves a new way to design multifunctional sonosensitizers and utilize them in various fields.

Recent studies indicate that the texture distribution in friction stir welded (FSW) Mg alloys can be tailored and hence improve the joint performance. In this work, a crystal plasticity finite element modeling (CPFEM) was performed to understand the effects of texture distribution in stir zone (SZ) on the non-uniform plastic deformation and fracture localization. In total, six kinds of observed or purposely tilted texture distributions were modelled. The “concave-convex” appearance, as commonly observed in the tensile sample, was successfully simulated. It reveals that the mirror-symmetrical distribution of basal planes in the region of easy to activate basal slip (EABS) determined the “concave-convex” appearance in SZ-center. The asymmetrical appearance exchanged on plane A and plane B when the directions of basal planes were switched in the two EABS regions. Furthermore, the asymmetrical feature of plastic deformation was changed with varying the texture distribution in SZ. The “embossed” feature became more obvious in SZ-center first, and then gradually weakened with the c-axis rotated away from the weld plate plane. Severe necking was successfully simulated in SZ-center of FSW-H joint and in SZ-side of FSW-L joint. That might determine the observed fracture morphology. We believe that this simulation study is helpful for further improving the performance of FSW Mg joints.

It is well known that twinning likely nucleates and forms pairs at grain boundaries (GBs) during the deformation of Mg alloys. Therefore, the crystallography of GBs plays an important role in selecting twin variants. In this regard, the Schmid factor (SF) cannot predict from which GBs twinning prefers to nucleate. To solve this problem, a composite Schmid factor (CSF) is proposed in this paper that incorporates both the SF and a geometric compatibility factor (m'). First, the link of the CSF with the SF and m' is defined and discussed based on theoretical derivations, which is further verified in terms of crystal plasticity finite element simulations. Then, experiments were carried out to investigate the effects of this parameter in predicting {10–12} extension twins in AZ31 Mg alloys. A CSF threshold for the activation of common-boundary twins was noticed and it decreases with the applied strain. By contrast, the SF threshold and its dependence with strain is not obvious. Moreover, the CSF is more effective in predicting the variants of common-boundary twins especially for small strain.

In this work, we report a texture dependent shifting behavior of neutral layer in bending of a Mg–3Al–1Zn (AZ31) alloy. The underline mechanism is revealed based on microstructure examinations, and is correlated to the yield asymmetry between the inner and outer regions. Then, a pole figure is built based on extensive viscoplastic self-consistent (VPSC) simulations, showing the trend of neutral layer shifting with texture. This is helpful for designing the stamping process of magnesium alloys.

The strengthening effects of different precipitates in ZK60 alloys on prismatic slip and {10–12} extension twinning (ET) were studied by experimental work and Visco-Plastic Self Consistent (VPSC) modeling. The results showed that the critical resolved shear stress (CRSS) of ET and prismatic slip are increased by 21 MPa and 9 MPa, respectively after aged for 10 h. The CRSS of ET was only slightly increased by 2 MPa after aged for 30 h; while it was increased by 32 MPa for prismatic slip. Yield asymmetry was sensitive to the variation of CRSS for these two deformation modes. These results are consistent with recent calculations by an Orowan hardening model. Moreover, it was found that disk-like β′2 precipitates have little influence on the total volume fraction of twins activated during compression along the rolling direction, while the precipitation of rod-like β′1 tends to reduce the twin volume fraction.

Goatpox disease and cystic hydatidosis may be simultaneously endemic in a given area. Their pathogens are goatpox virus (GPV) and Echinococcus granulosus (E. granulosus), respectively. Both E. granulosus EG95 subunit vaccine and live-attenuated GPV AV41 vaccine have been widely used for prevention of both diseases in China. However, it has been rarely reported that a bivalent vaccine is developed to prevent both diseases. The GPV is an ideal vector for developing recombinant multivalent vaccines to deliver immunogenic proteins in animals. In this study, we constructed an EG95 antigen-expressing recombinant GPV by the thymidine kinase gene-based homologous recombination in vitro. The recombinant GPV was purified and then proven to be able to express the EG95 antigen in vitro by Western blot and indirect immunoinfluscent assay, therefore expecting its potential as a bivalent vaccine candidate against both diseases in a future animal experiment.

Energy absorption characteristics of thin-walled tubes fabricated by bending of metal sheets are studied in this work. Square tubes are prepared by folding aluminum alloy 3003 H12 sheets and quasi-static axial crushing tests are then performed with universal material testing machine. The energy absorption capacity and deformation of these folded tubes are analyzed and compared with traditional square tubes. Nonlinear finite element code LS-DYNA is employed to simulate the crushing tests and the numerical results are compared with the experiment. The influences of various factors, including the boundary condition, loading speed, fillet radius and sectional shape, on the crushing force and deformation of the folded tubes are investigated numerically. In addition, folded tubes with multi-cell sections are also analyzed and compared with traditional tubes. Some suggestions are provided for the design of folded single or multi-cell tubes. Since the folded tubes are easily prepared, cost-effective for small-scale production and flexible in both sectional shape and geometric parameters, they are quite promising to be applied widely in various engineering fields.

Alzheimer's disease (AD) is one of the most common neurodegenerative disorders, which is characterized by the primary risk factor, age. Several attempts have been made to treat AD, while most of them end in failure. However, with the deepening study of pathogenesis of AD, the expression of HDAC6 in the hippocampus, which plays a major role of the memory formation, is becoming worth of notice. Neurofibrillary tangles (NFTs), a remarkable lesion in AD, has been characterized in association with the abnormal accumulation of hyperphosphorylated Tau, which is mainly caused by the high expression of HDAC6. On the other hand, the hypoacetylated tubulin induced by HDAC6 is also fatal for the neuronal transport, which is the key impact of the formation of axons and dendrites. Overall, the significantly increased expression of HDAC6 in brain regions is deleterious to neuron survival in AD patients. Based on the above research, the inhibition of HDAC6 seems to be a potential therapeutic method for the treatment of AD. Up to now, various types of HDAC6 inhibitors have been discovered. This review mainly analyzes the HDAC6 inhibitors reported amid 2010-2020 in terms of their structure, selectivity and pharmacological impact towards AD. And we aim at facilitating the design and development of better HDAC6 inhibitors in the future.

Predicting for long-term dynamics of complex systems from observations is a challenging topic in the field of time series modeling and analysis, and is continually under research. Noteworthily, multi-step prediction requires accurate learning of dynamics and correlations between historical data for predicting future behavior. In this paper, we proposed a modified recurrent neural network named hierarchical delay-memory echo state network (HDESN) for solving the task of multi-step chaotic time series prediction. The HDESN uses multiple reservoirs with delay-memory capabilities, which can simultaneously discover and explore the information of short-term and long-term memory hidden in the historical sequence, and extract the valuable evolution patterns through deep topology and hierarchical processing. Moreover, to ensure high-quality prediction results and reduce the computational burden as much as possible, we further design a phase-space representation strategy which can calculate a compact topology and delay-memory coefficient according to the chaotic characteristics of the data. Compared with other improved ESN-based models, the proposed HDESN does not have a larger memory capacity to capture potential evolution law hidden in the complex system layer by layer, but can also adaptively determine a suitable network architecture to reflect the mapping relations in chaotic phase space. The experimental results on two benchmark chaotic systems and a real-world meteorological dataset demonstrate that the proposed HDESN model obtains satisfactory performance in multi-step chaotic time series prediction.

The heteroatom doping to achieve carbon dots has played a crucial part in exploring the fluorescence mechanism of multicolor emissions, which is closely related with the application development of such materials. Herein, a facile method was developed to synthesize three-color CDs via the co-doping of S and N elements with the assistance of ethanol solvent. The achieved CDs mainly emit blue, green and red lights, yet the photoluminescence quantum yields (FLQYs) generally decreases from 54.0% for B-CDs, 11.5% for G-CDs to 8.5% for R-CDs, respectively. Optical and structural characterizations of these CDs show that the variation of sp2-sp3 hybridized domains resulted from the elements doping (S, N) fundamentally determines the color of CDs. The highest FLQYs of B-CDs in all CDs gives it an advantage to detect metal ions, which finds strong quenching only toward Ag+ because of the interaction between Ag+ and the cores. Likewise, these multicolor CDs attempt to fabricate CDs@PVP composite film emitted white-light as well as a clad material of WLED devices. The results indicate that the correlated color temperatures (CCT) in the ranges of cold and warm white lights can be obtained with different color rendering indexes (CRI) and luminous efficacies. Moreover, CCT and CRI are nearly up to the extremum at the same current value with increasing currents, implying that such CDs are potential as a kind of fluorescent nanomaterial to applicate to WLED device.

Static and dynamic loading of a type of folded tubes under three-point bending are investigated in this work. The folded tubes are easily prepared, cost-effective and flexible in sectional shape, and hence quite promising to be applied widely in various engineering fields. Experimental investigations are firstly performed for folded square tubes and numerical analyses by nonlinear finite element methods are then carried out. The influences of various factors, including the load direction, velocity, fillet radius and sectional shape, on the force and deformation responses of the folded tubes are investigated. The comparison between traditional tubes and folded tubes is performed and shows that the folded tubes outperform the traditional tubes in some aspects. In addition, some suggestions for the design of folded tubes are presented.

African swine fever virus (ASFV) is a large and complex DNA virus that causes a highly contagious and often lethal swine viral disease, for which no vaccine and effective treatments are available yet. Hence, ASFV presents significant economic consequences for the swine industry. A rapid and simple diagnostic method is urgently needed to monitor ASFV-specific antibodies for controlling the spread of ASFV. In this study, we chose the truncated p54 protein as an antigen and combined it with Eu-doped fluorescent microspheres as tracers to detect anti-ASFV antibodies specifically. Results showed that the truncated p54 protein had high specificity to ASFV antibody and had no cross-reactions with other swine virus antibodies. The results between our fluorescent immunochromatography test strip (FICTS) and commercial ELISA kits showed high consistency. The proposed FICTS offers a rapid, sensitive, specific, and visual method for ASFV antibody detection and shows great potential for ASF epidemic surveillance and control.

With the rapid development of information theoretic learning, the maximum correntropy criterion (MCC) has been widely used in time series prediction area. Especially, the kernel recursive least squares (KRLS) based on MCC is studied recently due to its online recursive form and the ability to resist noise and be robust in non-Gaussian environments. However, it is not always an optimal choice that using the correntropy, which is calculated by default Gaussian kernel function, to describe the local similarity between variables. Besides, the computational burden of MCC based KRLS will raise as data size increases, thus causing difficulties in accommodating time-varying environments. Therefore, this paper proposes a quantized generalized MCC (QGMCC) to solve the above problem. Specifically, a generalized MCC (GMCC) is utilized to enhance the accuracy and flexibility in calculating the correntropy. In order to solve the problem of computational complexity, QGMCC quantizes the input space and upper bounds the network size by vector quantization (VQ) method. Furthermore, QGMCC is applied to KRLS and forming a computationally efficient and precisely predictive algorithm. After that, the improved algorithm named quantized kernel recursive generalized maximum correntropy (QKRGMC) is set up and the derivation process is also given. Experimental results of one benchmark dataset and two real-world datasets are present to verify the effectiveness of the online prediction algorithm.

The cyclic extrusion compression (CEC) was applied to severely deform the as-extruded GW102K (Mg–10.0Gd–2.0Y–0.5Zr, wt%) alloy at 350, 400, and 450 °C, respectively. The microstructure, texture, and grain boundary character distribution of the CECed alloy were investigated in the present work. The mechanical properties were measured by uniaxial tension at room temperature. The crack initiation on the longitudinal section near the tensile fracture-surface was investigated by high-resolution scanning electron microscopy (SEM). The result shows that the microstructure was dramatically refined by dynamic recrystallization (DRX). The initial fiber texture was disintegrated and obviously weakened. The 8-passes/350 °C CECed alloy exhibited yield strength of 318 MPa with an elongation-to-fracture of 16.8%, increased by 41.3% and 162.5%, respectively. Moreover, the elongation-to-fracture of the 8-passes/450 °C CECed alloy significantly increased more than 3 times than that of the received alloy. The cracks were mainly initiated at twin boundaries and second phase/matrix interfaces during tensile deformation. The microstructure refinement was considered to result in the dramatically enhanced of the strength and ductility. In addition, the texture randomization during CEC is beneficial for enhancing ductility. The standard positive Hall–Petch relationships have been obtained for the CECed GW102K alloy.

Feature selection is an important preprocessing step in data mining. Mutual information-based feature selection is a kind of popular and effective approaches. In general, most existing mutual information-based techniques are greedy methods, which are proven to be efficient but suboptimal. In this paper, mutual information-based feature selection is transformed into a global optimization problem, which provides a new idea for solving feature selection problems. First, a single-objective feature selection algorithm combining relevance and redundancy is presented, which has well global searching ability and high computational efficiency. Furthermore, to improve the performance of feature selection, we propose a multi-objective feature selection algorithm. The method can meet different requirements and achieve a tradeoff among multiple conflicting objectives. On this basis, a hybrid feature selection framework is adopted for obtaining a final solution. We compare the performance of our algorithm with related methods on both synthetic and real datasets. Simulation results show the effectiveness and practicality of the proposed method.

The causality analysis is an important research topic in time series data mining. Granger causality analysis is a powerful method that determines cause and effect based on predictability. However, the traditional Granger causality is limited to analyzing linear causality between bivariate time series, because it is based on vector autoregressive models. In this paper, we propose a novel method, named Hilbert–Schmidt independence criterion Lasso Granger causality (HSIC-Lasso-GC), for revealing nonlinear causality between multivariate time series. Firstly, for each time series, we perform stationarity test and state space reconstruction to extract the historical information. Then, we build a HSIC-Lasso model of all input variables and output variable, where the optimal model is selected by generalized information criterion. Finally, according to the significance test, we get the causality analysis results from all input variables to output variable. In the simulations, we use two benchmark datasets and two actual datasets to test the effectiveness of the proposed method. The results show that the proposed method can effectively analyze nonlinear causality between multivariate time series.

For modeling of multivariate time series, input variable selection is a key problem. This paper presents the estimation of joint mutual information and its application in input variable selection problems. Mutual information is a commonly used measure for variable selection. To improve the performance of input variable selection, we propose a novel high-dimensional mutual information estimator based on copula entropy, which is estimated by the truncated k -nearest neighbor method. Simulations on high dimensional Gaussian distributions substantiate the effectiveness of the proposed mutual information estimator. A relationship between the joint mutual information and the copula entropy is derived, which is used for joint mutual information estimation. Then the proposed estimator is applied to input variable selection for multivariate time series modeling based on the criterion of max dependency and max–min dependency. A stop criterion is proposed to terminate the selection process automatically. Simulation results show that the input variable selection method works well on both synthetic and real life dataset.

A crystal plasticity finite element model (CPFEM) was developed to simulate the bending behaviors of a friction stir welded (FSW) Mg joint. The joint was divided into 12 sub-regions in the model to represent the observed complicated texture distributions. Twinning was modeled as pseudo slip and the predominant twin reorientation scheme was used. Two kinds of three-point bending geometries were applied. The present study aims to understand the underlying mechanisms of the strain heterogeneity and fracture localization commonly occurred in FSW Mg joints during bending deformation. It reveals that the strongly localized texture in conjunction with the different activities of slip and twinning in each sub-region cause severe strain heterogeneity in welding direction (WD), which results in the “concave-convex” appearance as observed on the longitudinal section. Besides, fracture initiates from the outer edge of thermomechanical affected zone (TMAZ) during Surface test due to the highly localized activation of basal slip, and crack likely propagates into the region of easy to activate basal slip (EABS) in a later stage. Crack initiates in crown zone (CZ)-side during Base test with a small distance away from the TMAZ/CZ interface. The huge differences in the activities of slip and twinning modes in each sub-region cause deformation incompatibility in the FSW Mg joint and initiates fracture in the area with maximum strain.

Solvothermally reduced and functionalized graphene (SRFG) was obtained by refluxing graphene oxide in dimethyl formamide at 140 °C. The resultant simultaneous nitrogen-doping and presence of dangling bonds on SRFG prompted us to study the evolution of structural changes with refluxing time. It is shown that the removal of oxygen-containing functional groups leaves dangling bonds during the solvothermal reduction, which are responsible for the nitrogen-doping with longer refluxing time. The relationship between the defects of SRFGs and their lithium ion storage performances are also discussed.

Graphene oxide (GO) was reduced with biocompatible glucose and polyvinylpyrrolidone (PVP) and incorporated in polylactic acid (PLA). The thermal reduction of GO during the compression molding of PLA was also studied to delineate the reduction efficiencies from thermal and chemical processes. Results indicate that glucose is more effective in the reduction of GO (rGO-g) with a much higher electrical conductivity than PVP and thermally treated GO. Even rGO-g was also highly efficient in improving the electrical conductivity of PLA. The composite with ∼1.25 vol.% of rGO-g exhibited a high conductivity of ∼2.2 S/m due to the chemical reduction of GO with glucose and the thermal reduction of rGO-g during the compression molding process.