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Field effect transistors based on few-layered van der Waals transition metal halide (TMH) Nb3Cl8 are studied in this work. Few-layered Nb3Cl8 exhibits typical N-type semiconducting behavior controlled by a Si gate, with the electrical signal enhancing as the thickness increases from 4.21 nm to 16.7 nm. Moreover, we find that the tunability of few-layered Nb3Cl8 FETs’ electrical transport properties can be significantly augmented through the use of an ionic liquid gate (or electrical double layer, EDL). This enhancement leads to a substantial increase in the on–off ratio by approximately a factor of 102, with the transfer curve modulated into a bipolar fashion. The emergence of such bipolar tunable characteristics in Nb3Cl8 FETs serves to enrich the electronic properties within the transition metal halide family, positioning Nb3Cl8 as a promising candidate for diverse applications spanning transistors, logic circuits, neuromorphic computing and spintronics.

The Group Sparse Representation (GSR) model shows excellent potential in various image restoration tasks. In this study, we propose a novel Multi-Scale Group Sparse Residual Constraint Model (MS-GSRC) which can be applied to various inverse problems, including denoising, inpainting, and compressed sensing (CS). Our new method involves the following three steps: (1) finding similar patches with an overlapping scheme for the input degraded image using a multi-scale strategy, (2) performing a group sparse coding on these patches with low-rank constraints to get an initial representation vector, and (3) under the Bayesian maximum a posteriori (MAP) restoration framework, we adopt an alternating minimization scheme to solve the corresponding equation and reconstruct the target image finally. Simulation experiments demonstrate that our proposed model outperforms in terms of both objective image quality and subjective visual quality compared to several state-of-the-art methods.

In this paper, we mainly consider two kinds of perturbed accelerated gradient descents driven by Lévy perturbations, which is of great importance for enhancing the global search ability. By using Lévy representation, Lévy perturbations can be divided into two parts: small jumps and large jumps, whose properties are then carefully discussed. By introducing the concept of attraction domain for local minima, Makovian transition properties are proven for the proposed two perturbed accelerated gradient descents with different infinitesimal matrices. Finally, all the results are extended to the vector case and two simulation examples are provided to validate all the conclusions.

Click fraud is one of the most common methods of cybercrime in recent years, and the Internet advertising industry suffers huge losses every year because of click fraud.In order to effectively detect fraudulent clicks within massive clicks, a variety of features that fully combine the relationship between advertising clicks and time attributes were constructed.Besides, an ensemble learning framework for click fraud detection was proposed, namely CAT-RFE ensemble learning framework.The CAT-RFE ensemble learning framework consisted of three parts: base classifier, recursive feature elimination (RFE) and voting ensemble learning.Among them, the gradient boosting model suitable for category features-CatBoost was used as the base classifier.RFE was a feature selection method based on greedy strategy, which can select a better feature combination from multiple sets of features.Voting ensemble learning was a learning method that combined the results of multiple base classifiers by voting.The framework obtained multiple sets of optimal feature combinations in the feature space through CatBoost and RFE, and then integrated the training results under these feature combinations through voting to obtain integrated click fraud detection results.The framework adopted the same base classifier and ensemble learning method, which not only overcame the problem of unsatisfactory integrated results due to the mutual constraints of different classifiers, but also overcame the tendency of RFE to fall into a local optimal solution when selecting features, so that it had better detection ability.The performance evaluation and comparative experimental results on the actual Internet click fraud dataset show that the click fraud detection ability of the CAT-RFE ensemble learning framework exceeds that of the CatBoost method, the combined method of CatBoost and RFE, and other machine learning methods, proving that the framework has good competitiveness.The proposed framework provides a feasible solution for Internet advertising click fraud detection.

Abstract Bearing is an important part of rotary machinery, which usually operates under a variety of complicated and severe conditions, and is prone to break down. To reduce and prevent the loss caused by the fault of bearings, a method based on multi‐band filtering (MBF) is proposed and applied to bearing fault diagnosis in this paper. Bearing signal is decomposed into multiple sub‐band signals by an MBF constructed from a specific prototype filter (finite impulse response filter) through cosine modulation. Then, the required sub‐band can be selected adaptively according to bearing fault frequency. To make the fault frequency more prominent, an adaptive filtering method is exploited to reduce the noise contained in the selected sub‐band. Finally, bearing fault diagnosis is realized by envelope spectrum analysis. Experimental results show that better performance in both simulated and real data are achieved by the proposed algorithm, which indicates that the proposed method can realize bearing fault diagnosis efficiently when compared to other state of the art methods.

Multi-modal image fusion integrates different images of the same scene collected by different sensors into one image, making the fused image recognizable by the computer and perceived by human vision easily. The traditional tensor decomposition is an approximate decomposition method and has been applied to image fusion. In this way, the image details may be lost in the process of fusion image reconstruction. To preserve the fine information of the images, an image fusion method based on tensor matrix product decomposition is proposed to fuse multi-modal images in this article. First, each source image is initialized into a separate third-order tensor. Then, the tensor is decomposed into a matrix product form by using singular value decomposition (SVD), and the Sigmoid function is used to fuse the features extracted in the decomposition process. Finally, the fused image is reconstructed by multiplying all the fused tensor components. Since the algorithm is based on a series of singular value decomposition, a stable closed solution can be obtained and the calculation is also simple. The experimental results show that the fusion image quality obtained by this algorithm is superior to other algorithms in both objective evaluation metrics and subjective evaluation.

In state analysis of rolling bearings using collaborative representation theory, how to construct an excellent redundant dictionary to collaboratively represent the acquired normal or abnormal data has been being a significant issue. Thus, a new method for fault detection and classification of rolling bearings is proposed in this paper. The proposed algorithm mainly consists of three components. First, a wavelet transform is employed to extract features, which takes advantage of the observation that vibration signals under different conditions have similar frequency spectra. This similarity ensures that we can collaboratively represent any test sample by using training samples. Second, under the similarity assumption, a dictionary pair learning strategy is employed to build an overcomplete dictionary pair, which is used to realize an optimal representation of the vibration signal. Meanwhile, the sparse constraint is also taken into account during dictionary training to enhance the robustness of the classification. Finally, the learned dictionary combined with collaborative representation is used to intelligently perform pattern classification of rolling bearings. The effectiveness and superiority of the method are verified by applying the proposed algorithm on the simulated and real vibration signals. The results show that, for different fault categories generated from different fault size and motor loads, our method can rapidly and accurately identify the fault category to which the input sample belongs.

A novel method based on directionlet transform is proposed for image denoising under Bayesian framework. In order to achieve noise removal, the directionlet coefficients of the uncorrupted image are modeled independently and identically by a two-state Laplacian mixture model with zero mean. The expectation-maximization algorithm is used to estimate the parameters that characterize the assumed prior model. Within the framework of Bayesian theory, the directionlet coefficients of noise-free image are estimated by a nonlinear shrinkage function based on weighted average of the minimum mean square error estimator. We demonstrate through simulations with images contaminated by additive white Gaussian noise that the proposed method is very competitive when compared with other methods in terms of both peak signal-to-noise ratio and visual quality.

An ultra performance liquid chromatography-quadrupole-time-flight mass spectrometric method was developed for the simultaneous screening and determination of 9 nitrobenzene compounds, including nitrobenzene (NB), 3, 4-dinitrotoluene (3, 4-DNT), 2, 3-dinitrotoluene (2, 3-DNT), 2, 4-dinitrotoluene (2, 4-DNT), 2, 6-dinitrotoluene (2, 6-DNT), 2, 5-dinitrotoluene (2, 5-DNT), 3, 5-dinitrotoluene (3, 5-DNT), 2-nitrotoluene (2-NT) and 1-chloro-4-nitrobenzene (ChNB) in cosmetics. Samples were extracted with ethyl acetate, and the extracts were further hydrogenated using 5% Pd/C catalyst. The target analytes were separated on a Poroshell 120 EC-C18 chromatographic column (100 mm × 4.6 mm, 2.7 μm). Gradient elution was carried out with 0.1% (VIV) formic acid aqueous solution and acetonitrile as the mobile phases. The eluent from the column was further detected using Q-tof MS with information dependent acquisition (Auto MS/MS) mode for MS data acquisition. A database, containing retention time, precise mass of parent ion, isotope abundance ratio and precise mass of fragment ion, was established for screening and confirming the nitrobenzene compounds. The quantitation analysis was carried out by extracting peak area with precise mass. Under optimal conditions, the analytes show good linear relationship within the range, with the correlation coefficient (r) not less than 0.999. The limits of detection are in the range of 0.01-0.3 mg/kg, and the limits of quantification are in the range of 0.04-2.0 mg/kg. Samples, such as cream, lotion, powder and lip balm, are fortified at three levels. The average spiked recoveries range from 85.2% to 113%, with the relative standard deviations (RSDs) ranging from 0.23% to 3.61%. The optimized method is accurate, sensitive, specific and suitable for the detection and screening of nitrobenzene compounds in cosmetics. [ABSTRACT FROM AUTHOR]