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Early bearing fault detection is crucial to avoid catastrophic accidents. However, the repetitive defect impulses indicating bearing fault are buried in heavy background noise. In the paper, a novel periodical sparse low-rank (PSLR) matrix estimation algorithm is proposed for extracting repetitive transients from noisy signal. Concretely, periodical group sparsity and low-lank property of fault transients in time–frequency domain are first revealed, and then an optimization problem is proposed for simultaneously promoting these two properties. Meanwhile, to further highlight the sparsity of fault features, the non-convex penalty functions are incorporated into the optimization problem. Then, for solving the proposed optimization problem, an iterative algorithm is derived based on alternating direction method of multipliers (ADMM) and majorization–minimization (MM), in which the traditional soft-thresholding operation is replaced by the proposed Gini-guided fault information thresholding (FIT) scheme to enhance fault transient extraction. Finally, simulated and real signals confirm the performance of proposed PSLR in extracting defect impulses from noisy vibration signal. • A novel periodical sparse low-rank (PSLR) matrix estimation algorithm is proposed. • The Gini-guided fault information thresholding (FIT) scheme is proposed. • The effectiveness of proposed PSLR is verified by simulated and real signals. [ABSTRACT FROM AUTHOR]

• Fifty pesticides and eight metabolites in eggs were simultaneously detected. • Multi-functional filter based on QuEChERS were used to rapid sample preparation. • The efficiency was obviously improved with shorter cleanup time of < 1 min. • Acetamiprid, pyrimethanil and fipronil sulfone were detected in actual egg samples. A sensitive method for the simultaneous determination of 58 pesticides and relevant metabolites in eggs was developed using ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) after clean-up with a multi-functional filter (MFF) based on quick, easy, cheap, effective, rugged, and safe method (QuEChERS). The egg sample was extracted with 5 ml water and 10 ml 1% acetic acid in acetonitrile and then salt out with sodium chloride. The extracted solution was filtered directly through an MFF containing 50 mg PSA, 50 mg C18, and 150 mg magnesium sulphate before UHPLC-MS/MS analysis. The clean-up and filter procedures were integrated using the MFF to substantially improve the work efficiency. Good linearity was shown for each analyte, and all the correlation coefficients exceeded 0.99. The recoveries in the eggs at the five spiked levels were 74.4%–115.2%, and the relative standard deviations (RSDs) were less than 15.3%. The limit of detection (LOD) and the limit of quantitation (LOQ) of 58 pesticides and 8 metabolites in eggs were 0.1–1.0 μg/kg and 0.2–5.0 μg/kg, respectively. The decision limit (CCα) and detection capacity (CCβ) were 3.4–111.1 μg/kg and 6.8–122.1 μg/kg, respectively. This method has also been successfully applied in the determination of actual eggs samples. This developed method is more effective and faster in the monitoring of pesticide residue in eggs compared to the traditional analytical method. [ABSTRACT FROM AUTHOR]

Highlights • Bearings fault diagnosis under variable speed and fluctuating load. • Teager Computed Order Spectrum and Stacking Auto-encoder. • Two other prepossessing methods are explored as comparisons. • Four other intelligent methods are explored as comparisons. Abstract Four main problems need to be solved in the fault diagnosis of rolling bearings when the speed and load both are changing: variable amplitude, fluctuating shocks interval, inconstant sample phase and signal noise pollution. To deal with these problems, this paper proposes a novel diagnosis method based on Teager Computed Order Spectrum (TCO Spectrum) and Stacking Auto-encoder (SAE). TCO-Spectrum, which is the pretreatment method for SAE, is the order spectrum of the signal after Teager energy operator demodulation. Two other pretreatment methods, Computed Order Spectrum and Hilbert Computed Order Spectrum, are also explored as comparisons to verify the effectiveness of TCO-Spectrum. In order to prove the powerful feature extraction ability of SAE, four traditional intelligent methods (Random Forest, Support Vector Machine, Hidden Markov Model and BP Network) are used as comparisons. The effectiveness of all methods is validated by the dataset composed of 56 training bearings and 8 testing bearings which involve 4 different health conditions. The speed range of each signal sample is controlled between 200 and 2000 rpm and the load is set from 7.7 kg to 48.1 kg. The diagnosis results demonstrate that the proposed method is capable to learn features adaptively from vibration signal of rolling bearings in spite of changing of speed and load. The proposed diagnosis method based on TCO spectrum and SAE achieve 95.72% recognition accuracy which is higher than other comparative methods. [ABSTRACT FROM AUTHOR]

Abstract Traditional measurement techniques such as fringe and stereo projection can hardly measure the black surface shape of magnetic liquid in revolving container which has a cover to prevent the liquid spilling out. To solve this problem, a multi-step measurement method based on linear laser projection is proposed. Modulated laser by synchronous revolving chopper is introduced to obtain the steady projection light curve on the measured location of the magnetic fluid surface. In order to capture the instantaneous reflected light of the projection light curve, a camera used a long exposure time of shooting is used. Taking the advantages of multiple exposure luminance enhancement effect of long exposure time, pictures with high brightness and contrast projection light are obtained. The overall surface shape of the fluid can be reconstructed by many laser projection lines extracted from the pictures captured at different angles of revolving container. A high transparency acrylic cover is adopted to provide good sealing performance and light transmission quality in the experiment. On the base of correction of influence of refractive index of the acrylic cover, the height formula of magnetic fluid surface is derived. The camera installation angle and distance between the camera and the container are not required as the size of container are utilized to calibrate the camera. The relationship between image coordinates and world coordinates is also obtained. According to the height curves of the projection lines and their measuring angles on the container, 3-D surface shape of magnetic fluid is reconstructed. The surface shape is verified indirectly by using the imbalance of the container, and the relative unbalance errors between the measurement results and the results by Shenk dynamic balancing machine under different conditions are between 0.8% and 11%. Another performance experiment on measuring the surface shape of an rubber block indicates that the measurement range of the proposed method is that the slope of the measured surface must be less than 3.94, and the sensitivity is 0.18 mm. All performance of the proposed method is acceptable, so such a complex industrial problem is solved only by a daily digital camera. Highlights • Multi-step measurement method based on chopper modulated linear laser projection. • Corrected height formula of the measured surface. • Camera calibration without installation angle and distance. • Verification experiments and performance analysis. [ABSTRACT FROM AUTHOR]

Ibuprofen (IBU), as a commonly used non-steroidal anti-inflammatory drug (NSAID) and pharmaceutical and personal care product (PPCP), is frequently prescribed by doctors to relieve pain. It is widely released into environmental water and soil in the form of chiral enantiomers by the urination and defecation of humans or animals and by sewage discharge from wastewater treatment plants. This study focused on the alteration of metabolism in the adult zebrafish ( Danio rerio ) brain after exposure to R-(-)-/S-(+)-/rac-IBU at 5 μg L −1 for 28 days. A total of 45 potential biomarkers and related pathways, including amino acids and their derivatives, purine and its derivatives, nucleotides and other metabolites, were observed with untargeted metabolomics. To validate the metabolic disorders induced by IBU, 22 amino acids and 3 antioxidant enzymes were selected to be quantitated and determined using targeted metabolomics and enzyme assay. Stereoselective changes were observed in the 45 identified biomarkers from the untargeted metabolomics analysis. The 22 amino acids quantitated in targeted metabolomics and 3 antioxidant enzymes determined in enzyme assay also showed stereoselective changes after R-(-)-/S-(+)-/rac-IBU exposure. Results showed that even at a low concentration of R-(-)-/S-(+)-/rac-IBU, disorders in metabolism and antioxidant defense systems were still induced with stereoselectivity. Our study may enable a better understanding of the risks of chiral PPCPs in aquatic organisms in the environment. [ABSTRACT FROM AUTHOR]

Recently, Instantaneous Angular Speed (IAS) measurement is successfully established and prevalently applied to a wide variety of machines due to the hypothesis that the speed fluctuation of rotating machinery carries plentiful dynamic responses. Nevertheless, exploration and application based on angular signal are still insufficient. Under the same hypothesis, in this paper, we introduced an extended algorithm named Instantaneous Angular Phase Demodulation (IAPD), together with the selection of optimal sideband family to extract the Instantaneous Angular Displacement Fluctuation (IADF) signal. In order to evaluate the performance of IADF signal, an effective approach was demonstrated using IADF signal to address the fault detection and diagnosis issue. After extracting the IADF signal, a much effective method was developed to deal with the large amount of data generated during the signal collection process. Then, we used the well-developed techniques, i.e., empirical mode decomposition (EMD) and envelope analysis, to undertake the signal de-noising and feature extraction task. The effectiveness and capability of the IADF signal were evaluated by two kinds of gearboxes under differentconditions in practice. In particular, the prevalent IAS signal and vibration signal were also involved and testified by the proposed procedure. Experimental results demonstrated that by means of the IADF signal, the combination of EMD and envelope analysis not only provided accurate identification results with a higher signal-to-noise ratio, but was also capable of revealing the fault characteristics significantly and effectively. In contrast, although the IAS signal had the potential ability to diagnose the serious fault, it failed for the slight crack case. Moreover, the same procedure even its improvements, i.e., ensemble empirical mode decomposition and local mean decomposition, all failed to recognize the faults in terms of vibration signals. [ABSTRACT FROM AUTHOR]

Our previous research and extensive epidemiological studies reproducibly demonstrated that miR-502 potentially targeted the expression of H4K20 methyltransferase SET8 in a wide spectrum of cancer. Yet, the direct targeting of SET8 by miR-502 has not been definitively proven. The clinical significance of the miR-502/SET8 regulatory circuit is also not clear. Here, we conducted cell-based experiments and clinical studies in a cohort of 279 breast cancer samples. We provide evidence that SET8 is a direct target of miR-502. Treatment with miR-502 or downregulation of SET8 suppressed cell proliferation and cell cycle, and reduced cell migration, invasion and EMT. Clinical analyses showed the miR-502 expression was lower in tumor tissues than in adjacent non-tumor tissues and had a significant inverse correlation with that of SET8. Furthermore, high expression of SET8 was significantly associated with poor overall survival (OS) and disease free survival (DFS) of breast cancer. The low expression ratio of miR-502 to SET8 mRNA was also significantly associated with poor OS. Thus, the miR-502/SET8 regulatory circuit emerges as a key regulator of the pathobiology of cancer and a focal point for possible therapeutic intervention. [ABSTRACT FROM AUTHOR]

Accurate sea surface temperature (SST) prediction is of great significance for fishery farming, marine ecological protection, and planning of maritime activities. In this paper, the stacked generalization ensemble is demonstrated for SST prediction to improve every single deep-learning model. Long-term high-resolution satellite-derived SST is used with the sub-regions of the Taiwan Strait and East China Sea taken as the study area. We select the Multilayer Perceptron, Long short-term memory (LSTM), Convolutional Neural Networks (CNN), CNN-LSTM as individual learners, and Convolutional LSTM as the meta-learner. The individual learners are trained and validated on the retained data subset I, while the meta-learner is trained and validated by constructing the samples with the predictions of validated individual learners on the retained data subset II. The two types of models are evaluated on the same test dataset with root-mean-square error and coefficient of efficiency as the scoring criteria. We find that the meta-model outperforms any individual model and other baselines for the one-day-/three-day-ahead forecasts in the Taiwan Strait and one-day-/three-day-/five-day-ahead predictions in the East China Sea. Furthermore, when the lead time is 1 day and 3 days, the meta-model has a better spatial distribution of prediction metrics across all grid points in the Taiwan Strait sub-area. For the East China Sea sub-region, the meta-model advantage is extended to the lead time of 5 days. Probably due to the higher quality of offshore satellite data, the prediction ability enhancement of the stacked ensemble applied in the East China Sea is better than that in the Taiwan Strait. The better-performing meta-model prediction suggests that the stacked generalization ensemble is encouraging and promising for improving the short-term prediction of the daily SST field. • The stacked ensemble of multiple models is conducted for SST prediction. • The stacked ensemble enhancement effects are investigated for different oceans. • Results indicate that stacking can effectively enhance short-term performance. • The improvement effect of the open sea area is better than the near-shore one. [ABSTRACT FROM AUTHOR]

Abstract We were successful in realizing an efficient warm white organic light-emitting device simultaneously exhibiting extreme color-stability and high color rendering index (CRI) just by precisely confining the recombination zone and fully utilizing the energy transfer between excitons. The warm white OLED presents maximum efficiencies of 21.4 cd/A, 25.8 lm/W and still remains at high efficiencies of 17.7 cd/A, 15.6 lm/W at the luminance of 1000 cd/m2. As the operating current density increases from 10 mA/cm2 to 60 mA/cm2, the variations in Commission Internationale de l'Eclairage (CIE) coordinates are only (0.004, 0.007) while the general CRI (R a) maintains at 80 approximately and the special CRI R 9 is higher than 20. What is more, the Duv satisfies the criteria for CRI measurements and the gamut area index (GAI) ensures the vividness of the illuminated objects, as renders the fabricated white OLED a promising candidate for high-quality solid-state lighting application. We also studied the origin of the quite slight color variations for the warm white OLED. Graphical abstract Image 1 Highlights • A white organic light-emitting device possessing high color stability was realized. • The R a maintains constant at high values around 80 while R9 exceeds 20. • Duvs and GAI satisfy the requirement for high-class lighting application. [ABSTRACT FROM AUTHOR]

Abstract The effects of using Cesium azide as an n-dopant in Bis-4,6-(3,5-di-3-pyridylphenyl)-2-methylpyrimi-dine on the performances of green phosphorescent organic light-emitting device had been studied in detail for the first time. By optimizing the device structure, phosphorescent organic light-emitting device having a maximum current efficiency of 76.4 cd/A and a maximum external quantum efficiency of 21.7% was realized. In particular, the maximum power efficiency and the power efficiency at 100 cd/m2 were as high as 99.9lm/W and 99.4lm/W, respectively, which were better than the parameter on power efficiency of most reported Ir (ppy) 3 -OLEDs without out-coupling enhancement. Origins of the improvement on device characteristics in the n-doped device were systematically investigated by experimental and theoretical methods and the role of Cesium azide in enhancing the electron conductivity of the n-doped electron-transporting layer was discovered by X-ray photoelectron spectroscopy measurement. Graphical abstract Image 1 Highlights • An efficient green phosphorescent organic light-emitting device with efficiencies of 76.4 cd/A, 99.9lm/W and 21.7% was realized. • The driving voltage at 100 cd/m2 (2.4 V) is only 0.45 V above the theoretical limit voltage required by a green OLED at 100 cd/m2. • The difference in electron injection barrier at ETL/cathode contacts was analyzed by employing photovoltaic measurement. • A novel mechanism on the role of CsN 3 in boosting the electron conductivity of electron-transporting layer was proposed by performing XPS measurement. • The n-doped device has reduced efficiency roll-off and the origin of the improvement in efficiency roll-off characteristic was demonstrated by utilizing the triplet-triplet annihilation (TTA) model. [ABSTRACT FROM AUTHOR]

• Simultaneous detection of solute and concentration using microimages of light-emitting polymer microwires. • Deep learning algorithms analyze fluorescence images of polymer microwires for label-free identification of solutes. • Utilizing multimodal networks enables precise concentration sensing without predefined response curves. We present a deep learning (DL)-assisted solution sensor that enables the simultaneous perception of solute and concentration in diverse environments using a parallel-coupled rhodamine (RhB)-doped polymer microwires (PC-PMWs). When immersed in NaCl or KCl solutions, the PC-PMWs exhibit periodic energy distribution, which is visually indicated by fluorescence and can be directly determined from microimages. By analyzing the microimages using a convolutional neural network (CNN), we achieved a solute classification accuracy of 95.6 % for NaCl and KCl. Utilizing microimages and physical parameters, our multimodal model composed of CNNs and multilayer perception networks provides accurate concentration predictions for NaCl and KCl solutions in the concentration range of 1.5 %–3.0 %. The evaluation metrics for NaCl concentration prediction are as follows: RMSE = 8.09 × 10−2 %, MAE = 6.17 × 10−2, EV = 0.962, and R2 = 0.961. For KCl solutions, the metrics are RMSE = 9.8 × 10−2 %, MAE = 7.67 × 10−2, EV = 0.953, and R2 = 0.942. Our DL-assisted PC-PMWs solution sensor can provide information about environmental components and concentrations simultaneously, without following a sensing response curve or modifying the sensing unit for oriented detection. This capability enhances the utility of micro/nano-optical sensors for multi-information sensing in diverse environments. [ABSTRACT FROM AUTHOR]

[Display omitted] • Noble metal-semiconductor hybrid Au/ZnO nanowires have been fabricated by chemical vapor deposition method. • Compared with ZnO nanowires, Au/ZnO heterostructures exhibited significantly enhanced photocatalytic performance due to the synergistic combination of Au and ZnO. • Optical properties were modeled to support our interpretation of photocatalytic mechanisms. • Our work provides a feasible strategy to prepare plasmonic photocatalysts and will also deepen the understanding of the plasmon enhanced photocatalytic performance in hybrid photocatalysts. Using sunlight as renewable resource has stimulated the development of sustainable photocatalysts that can collect visible light. Semiconductor nanowire-based photocatalysts have attracted increasing interest due to their unique optical, electronic, and structural advantages. Here, noble metal-semiconductor hybrid Au/ZnO nanowires have been fabricated by chemical vapor deposition method. The hybrid photocatalysts were well characterized by SEM, TEM, EDS, XRD, XPS, UV–visible spectroscopy and Raman spectroscopy. Compared with ZnO nanowires, Au/ZnO heterostructures exhibited significantly enhanced photocatalytic performance due to the synergistic combination of Au and ZnO. Optical properties were modeled to support our interpretation of photocatalytic mechanisms. Our work provides a feasible strategy to prepare plasmonic photocatalysts and will also deepen the understanding of the plasmon enhanced photocatalytic performance in hybrid photocatalysts. [ABSTRACT FROM AUTHOR]

• A new unbiased autocorrelation morphological filter (UAMF) is proposed. • The UAMF is combined with autoregressive filter to eliminate deterministic noise. • Unbiased autocorrelation kurtosis is developed to determine the optimal SE scale. Traditional morphological filter methods (MFs) are commonly affected by deterministic signals and random impulses, so an adaptive unbiased-autocorrelation morphological filter (UAMF) combined with an autoregressive filter (AR-UAMF) is proposed to tackle the issue. The deterministic component of the raw signal is first removed using an autoregressive filter (AR), and then the random impulse interference is attenuated using the UAMF. Additionally, unbiased-autocorrelation kurtosis (AK) is developed to ascertain the optimum SE scale. This indicator has the advantage of being sensitive to fault-related impulses under the interference of deterministic signals, random impulses and white noise. Finally, the simulation and experiment analysis results demonstrate that AR-UAMF is much more powerful and effective than the other single-scale and multiscale MFs in recovering the fault-related impulses and performing fault diagnoses. [ABSTRACT FROM AUTHOR]

Based on the Conditional Restricted Boltzmann Machine - Deep Belief Network (CRBM-DBN), we select four patterns and compare their prediction effects for the significant wave height in the Gulf of Mexico (GoM). Historical datasets of all 12 buoys managed by the National Data Buoy Center are employed to train and construct models. Root-mean-square error (RMSE) and coefficient of efficiency (CE) between the observed and predicted wave heights are investigated. We find that for the short-term prediction (i.e., lead time≤12 h), the best results (RMSE<0.24 m and CE > 0.92) are achieved with the univariate significant wave height as the input in most cases of the whole gulf. When the lead time is equal to 24 h or 48 h, the multivariate pattern of "significant wave height + dominant wave direction + wind speed + wind direction" has the optimal effects (0.18 m < RMSE<0.40 m and 0.72 < CE < 0.93) in the vicinity of 26。N oceans. The superiority is very obvious and gradually diminishes as the latitude increases to the north and decreases to the south. Regarding the wave height predictions in different oceans of GoM, the findings provide evidence that it may be contributed to select optimal prediction patterns and obtain the best applications. • Based on the CRBM-DBN, we investigate the prediction effects of four patterns. • Univariant wave height, and multivariant wave and wind patterns have respective optimal effects. • The research results are helpful to select the best patterns for different predictive requirements. [ABSTRACT FROM AUTHOR]