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To solve the difficulty in correctly identifying a compound fault of rolling bearing, a method combining variational mode decomposition (VMD) and harmonic fusion vector bispectrum (HFVB) is proposed. Firstly, to achieve adaptive decomposition of signals, the characteristic ability of envelope entropy to represent signal sparsity is utilized. By employing the minimum envelope entropy as the fitness function for the sparrow search algorithm (SSA), the decomposition levels and penalty factors of VMD are adaptively determined. Secondly, root-mean-square value is treated as fault feature index to self-adaptively choose from intrinsic mode function (IMF) which can embody fault features of bearings. Thirdly, to further highlight fault features, HFVB is used to blend chosen IMFs. Finally, faults of bearings were recognized with spectrum of signals. To verify the effectiveness of proposed method, an analysis was given to vibration signals in different situations, and SSA-VMD-HFVB was compared with classical method. The results demonstrate that the proposed SSA-VMD-HFVB method facilitates the adaptive decomposition of VMD, enabling the selection and effective integration of sensitive fault component signals. This approach enhances the accuracy of complex fault diagnosing in rolling bearings. [ABSTRACT FROM AUTHOR]

The characteristic frequency of a rub-impact fault is usually very complex and may contain higher harmonics and subharmonics. Due to the uncertainty of harmonic components and the complexity of signal-to-noise ratio (SNR) operation, the general scale transformation stochastic resonance (GSTSR) has certain limitations in the identification of rub-impact faults. To solve this problem, the paper starts with complexity and proposes a rub-impact fault identification method combining a swarm intelligence optimized algorithm (SIOA) with Hjorth parameters and GSTSR. The complexity of vibration signals will change greatly before and after rub-impact faults. The complexity parameter in Hjorth parameters can effectively embody the complexity of signals and is invulnerable to noise interference. Therefore, the complexity parameter in the Hjorth parameters is taken as the objective function of SIOA and combined with GSTSR. Vibration signals from cases are taken as input to adaptive stochastic resonant (ASR) systems, and the system parameters are adaptively and synchronously adjusted to realize the maximal resonant effect. Finally, the spectrum analysis of signals obtained from ASR is used to extract failure features and recognize faults in the rotor–stator rub-impact. The proposed method is verified by comparing it with other schemes under different SIOAs and different operating conditions. The result of the comparison shows that the complexity parameter of the Hjorth parameters can be taken as the objective function of SIOA to accurately identify the rub-impact fault. Meanwhile, the proposed method, compared with the method of taking SNR as an objective function, has a better effect on reducing time costs and strengthening fault characteristics. [ABSTRACT FROM AUTHOR]

When a compound failure occurs in a bearing, the failure information included in vibration signals is featured by being weak, complex and combined. This fact makes it difficult to identify a compound failure precisely. Variational mode extraction (VME) solves the problem of variational mode decomposition (VMD) in being difficult to determine the number of decomposition layers and has certain applications in the identification of bearing failures. However, the initial expected central frequency of VME and option of penalty factor is crucial to the extraction of expected mode. To precisely determine the central frequency and penalty factor of VME and fulfill the correct extraction of feature information of compound failures in bearing, the paper has proposed a method based on synchro squeezing transform (SST) and information entropy to adaptively determine the parameters in VME (SST-VME). Firstly, SST is used to make time-frequency analysis of original vibration signals and characteristic frequency bands with larger energy are chosen from time-frequency spectrum to adaptively determine the expected central frequency of VME. Secondly, as information entropy has representation capacity for information included in the signal, penalty factor of VME was adaptively determined according to information entropy. Thirdly, original signals were subjected to VME according to expected central frequency and penalty factor adaptively determined; the expectation mode obtained was denoised by singular value decomposition algorithm. Finally, the type of compound failures of bearing is determined according to the frequency spectrum of denoised signals. To verify the effectiveness of proposed method, a comparison with VMD algorithm is conducted. As indicated by the result, the proposed method is more precise and comprehensive than VMD algorithm to extract the feature information corresponding to compound faults of bearing, and thereby correctly determines the type of compound failures. [ABSTRACT FROM AUTHOR]

Bearing is the most vulnerable key part in rotating machine and bears important influence on the safety of equipment. Weakness and complexity are the two features of fault characteristic information carried by signals in the early stage of fault. For that, a fault is difficult to be recognized correctly. To identify a compound failure of bearing, the paper has brought forward a new self-adaptive option method for component signals that are sensitive to failure feature information of bearing. The sensitivity of kurtosis to bearing failure is exploited and the influence of signal complexity on the extraction of failure feature information is taken seriously, the paper has proposed the self-adaptive option method for component signals that are sensitive to failure feature by combined kurtosis with Complexity parameter included in Hjorth parameters. Furthermore, as the mid-value represents the general level of signal and is not affected by larger or smaller data, with the mid-values of kurtosis and Complexity parameter as the boundary, the paper has chosen the component signals which can more comprehensively show the failure features of bearing. Additionally, by principal component analysis (PCA), component signals selected are blended and reconstructed. Finally, by the Hilbert envelope spectrum of signals reconstructed, failure types of bearing are identified. To verify the effectiveness of presented method, the presented method is compared with conventional method on the basis of the exactly consistent data. The result indicates that the proposed method is superior to the traditional one in extracting fault information and identifying the multiple failure types of bearing. [ABSTRACT FROM AUTHOR]

A series of molecular dynamics simulations were performed using the ReaxFF reactive force field to investigate the pyrolysis of Longkou oil shale kerogen in the presence of methanol. The roles of methanol in kerogen pyrolysis were deeply explored to reveal the microscopic mechanism of methanolysis by analyzing the distribution of products, the consumption of methanol, and the formation processes of typical products at different temperatures. The results demonstrate that the yield of shale oil in the presence of methanol is consistently higher than that of kerogen direct pyrolysis at the same temperature, which is consistent with the trend of oil yield with temperature reported by previous methanolysis experiments. Methanol supplies a large number of small radicals under the pyrolysis condition, which can facilitate the breakage of weak bonds and take part in the formation of products through combining with the radical fragments generated by the pyrolysis of kerogen. This study will provide theoretical guidance for understanding the methanolysis process of oil shale. [ABSTRACT FROM AUTHOR]

For effective identification of rub-impact faults between rotor and stator of equipment, the paper has contributed the method integrating dual complexity parameters and variational mode decomposition (VMD). Firstly, to effectively separate the fault characteristics involved in signals, VMD algorithm was applied to decompose vibration signals and the component signals were obtained; secondly, taking account of the large difference of fault feature information involved in different component signals and in order to make options of sensitive fault component signals and reduce the loss of fault features, multi-scale Lempel–Ziv complexity and complexity parameter in Hjorth parameters were brought. Starting from 2 different perspectives of complexity evaluation, choose from the sensitive component signals containing more fault characteristics with these 2 complexity parameters; thirdly, signals were reconstructed based on selected sensitive component signals, and meanwhile, singular value difference spectrum algorithm was used to denoise reconstructed signals to further lessen the influence of noises; finally, the rub-impact fault between rotor and stator was identified by square demodulation spectrum (SDS) of denoised signal. The effectiveness of the proposed method has been proved by comparative analysis with other approaches as well as validation analysis of rub-impact fault signals in multiple situations. [ABSTRACT FROM AUTHOR]

Large-sized rotating machines usually contain weak feature information of rub-impact fault, which is hard to extract. General scale transformation stochastic resonance (GSTSR) can match input signals with different frequencies by using the optimal barrier height and boost the weak fault feature in signals. The performance of GSTSR is determined by systemic parameters. When a rub-impact fault occurs between rotor and stator, vibration signals are often accompanied by an impact. Therefore, the paper takes advantage of sensibility of waveform factor to rub-impact fault information and margin factor to impact properties of signal and reconstructs a new signal evaluation index based on waveform and margin factors. The signal evaluation index is treated as the fitness function of grey wolf optimization (GWO) algorithm and combined with GSTSR to perform a comprehensive evaluation to rub-impact fault feature information. The result of comparison with conventional method (with signal to noise ratio (SNR) as fitness function) indicates that in case of extracting rub-impact fault features, the proposed method identifies a rotor-stator rub-impact fault more precisely than the classical method. [ABSTRACT FROM AUTHOR]

To solve the difficulties of extracting the fault feature information from compound failures of rolling bearings, a new method by combining intrinsic time scale decomposition (ITD), Hjorth parameters and Teager energy spectrum (ITD-Hjorth-TES) is proposed. The method starts with combining wavelet global threshold denoising algorithm and ITD, the signals denoised are decomposed by means of ITD. Secondly, the complexity parameter is determined from Hjorth parameters of proper rotation components (PRCs) obtained by decomposition. Thirdly, pick up the PRCs associated with the two maximum complexity values and calculate the cross-correlation function of two PRCs. Finally, the feature corresponding to the compound failures of the rolling bearing is extracted and the fault type is identified with the Teager energy spectrum of obtained cross-correlation function. The result indicates that the feature from compound failures of rolling bearings can be extracted efficiently and an accurate judgment on the type of compound failure can be implemented. [ABSTRACT FROM AUTHOR]

To accurately locate rotor–stator rubbing faults in aero-engine, a method combining intrinsic time scale decomposition (ITD), Hjorth parameter and cepstrum analysis has been proposed. First, the method works by decomposing vibration signals from casing into proper rotation components (PRCs) based on ITD; second, calculates the autocorrelation function of each rotation component and the complexity parameters of autocorrelation functions; third, chooses PRCs for signal reconstruction according to complexity from Hjorth parameters and reconstructs the signals based on chosen PRCs; at last, considering that with different positions of rubbing, transfer paths of signals collected by the sensors from the same position are different, cepstrum analysis has been made according to reconstructed signals, and the transfer paths characteristics from cepstrum are taken as feature vectors and inputted into support vector machine for identifying the positions of rotor-stator rubbing faults. The results indicate proposed ITD-Hjorth-Cepstrum method can work well in the identification rate of training and test samples as for the identification rate for an unknown sample. [ABSTRACT FROM AUTHOR]

To properly identify the combined faults of rolling bearings, the paper has proposed a new approach to extract the fault characteristics of rolling bearings based on the combination of discrete wavelet transform (DWT) and generalized S transform (GST). To effectively separate the signals of different frequency bands, original acceleration signal was subjected to wavelet decomposition to obtain approximate and detailed signals. Furthermore, a time–frequency analysis was given to approximate and detailed signals through generalized S transform due to its excellent time–frequency property. At last, according to time–frequency slice diagram of GST, feature frequency of the rolling bearing could be effectively extracted under different combined fault types. The result indicates that the combination of wavelet transform and generalized S transform can identify the combined fault types for bearings more precisely than generalized S transform alone. [ABSTRACT FROM AUTHOR]

To effectively extract the information of compound faults of inter-shaft bearing of an aero-engine based on casing vibration signals, the paper has introduced the concept of weighted Katz fractal dimension and proposed the method combining information fusion, wavelet transform (WT), singular value decomposition (SVD), and Katz fractal dimension, the cross-correlation function (CCF-WT-SVD-Katz algorithm). The method includes homologous information fusion achieved by the CCF of horizontal and vertical vibration signals of the rotor from the same section; signal separation and denoising of blended signals through WT and SVD; reinforcement of fault characteristics of signals according to weighted Katz fractal dimension; and extraction of characteristic frequencies of compound faults of inter-shaft bearing by frequency spectrum of weighted and reconstructed signals. The result indicates that the proposed CCF-WT-SVD-Katz algorithm is capable of effectively extracting compound fault characteristics of inter-shaft bearing and precisely identifying a fault type based on whole casing vibration signals and will be of very good application value in engineering. [ABSTRACT FROM AUTHOR]

To precisely determine the type of bearing fault, the paper has proposed the solution by combining first-order difference of signals and coarse-graining. When a bearing fault occurs, vibration signals are often accompanied by impact features and meanwhile, greater difference among adjacent points of signals corresponds to more evident impact features. Compared with original vibration signals, the first-order difference of signals works better in measuring this kind of impact feature. Therefore, the solution proposed in the paper takes first-order difference in place of original signals and combines with coarse-graining algorithm for subsequent treatment. Compared with average value of signals, the variance of signal can represent the discreteness of signal which describes the impact feature of bearing fault from the other side. Therefore, the solution has coarse-graining operation for difference signals in improved coarse-graining method with variance as criterion to further highlight impact features of rolling bearing. Secondly, a feature set has been established to comprehensively represent fault information of bearing with the average of coarse-graining sequence as feature parameter. Finally, the type of bearing fault is identified according to the fault feature set and support vector machine (SVM). To verify the effectiveness and universality of solution proposed by the paper, a comparison is given to proposed solution and classical method according to dataset of faults of rotor and rolling bearing, and public dataset. The result indicates that with different rotate speeds and types of faults, the proposed solution is more precise to identify the type of bearing faults; in two different datasets, the average identification rate of the proposed solution is 96.43% (aeroengine–rotor–rolling bearing fault dataset) and 100% (public dataset) for unknown samples. [ABSTRACT FROM AUTHOR]

The oriental tobacco budworm Helicoverpa assulta (Lepidoptera: Noctuidae) is a specialist pest that may cause serious damages to important crops such as chili pepper and tobacco. Various man-made insecticides have been applied to control the infestation of this pest. To understand how this pest copes with insecticides, it is required to identify key players involved in insecticide transformation. In this study, a P450 gene of CYP6B subfamily was identified in the oriental tobacco budworm, and its expression pattern was revealed. Moreover, the activities of HassCYP6B6 against 12 insecticides were explored using recombinant enzymes produced in the facile Escherichia coli. Data from metabolic experiments showed that HassCYP6B6 was able to metabolize conventional insecticides including organophosporates (diazinon, malathion, phoxim), carbamate propoxur, and pyrethroid esfenvalerate, while no significant metabolism was observed towards new-type pesticides such as neonicotinoids (acetamiprid, imidacloprid), diamides (chlorantraniliprole, cyantraniliprole), macrocyclic lactone (emamectin benzoate, ivermectin), and metaflumizone. Structures of metabolites were proposed based on mass spectrometry analyses. The results demonstrate that HassCYP6B6 plays important roles in the transformation of multiple insecticides via substrate-dependent catalytic mechanisms including dehydrogenation, hydroxylation and oxidative desulfurization. The findings have important applied implications for the usage of insecticides. [Display omitted] • A new P450 gene (HassCYP6B6) is identified in Helicoverpa assulta. • The recombinant HassCYP6B6 protein is successfully produced in Escherichia coli. • HassCYP6B6 enzyme is able to metabolize multiple insecticides. • Hydroxylation and desulfuration are common pathways in HassCYP6B6 catalyzed insecticide metabolism. • A unique alkyl dehydrogenated metabolite of propoxur is firstly discovered. [ABSTRACT FROM AUTHOR]

Intrinsic time-scale decomposition and graph signal processing are combined to effectively identify a rotor–stator rubbing fault. The vibration signal is decomposed into mutually independent rotational components, and then, the Laplacian energy index is obtained by the graph signal of the autocorrelation function of rotational components, and the signal is reconstructed by an autocorrelation function of each proper rotation (PR) component relative to smaller Laplacian energy index (less complexity). Finally, characteristics are extracted from rotor–stator rubbing faults in an aeroengine according to square demodulation spectrum of a reconstructed signal. To validate the effectiveness of the algorithm, a comparative analysis is made among traditional intrinsic time-scale decomposition algorithm, combination of intrinsic time-scale decomposition and autocorrelation function, and the proposed intrinsic time-scale decomposition–graph signal processing algorithm. Comparative result shows that the proposed intrinsic time-scale decomposition–graph signal processing algorithm is more precise and effective than the traditional intrinsic time-scale decomposition and intrinsic time-scale decomposition and autocorrelation function algorithms in extracting characteristic frequency and frequency multiplication of rotor–stator rubbing faults and can greatly reduce the number of noise components irrelevant to faults. [ABSTRACT FROM AUTHOR]

To effectively identify the rotor–stator rubbing fault, the paper has brought forward a method combining principal component analysis (PCA), intrinsic time-scale decomposition (ITD), and information entropy (IE). Firstly, in considering that the characteristic information of faults extracted from the information collected by single sensor is not complete or comprehensive, the approach blends the vibration signals collected from 4 different positions at the same moment based on PCA algorithm; secondly, regarding that ITD algorithm can effectively avoid the problems of poor adaptivity and end effect, blended signals are broken down based on ITD algorithm; thirdly, calculate the IE of self-correlation function of each PRC based on the fact that the smaller IE is, the less confusion system has and the easier it is to extract fault characteristics, and treat the self-correlation function of PRC related with the minimum IE as optimal component to represent fault characteristics; fourthly, characteristic extraction of rotor–stator rubbing fault and identification are done on the basis of the frequency spectrum of optimal component. To prove the availability of method, vibration signals are subjected to validation and analysis, which are collected from different rotation speeds, casing thicknesses, rubbing positions, and types. The result indicates that the proposed PCA–ITD–IE can equally and effectively extract the characteristics of rotor–stator rubbing faults of aero-engine involved in various conditions. [ABSTRACT FROM AUTHOR]

To fulfill the effective diagnosis of the rubbing fault between the rotor and the stator, the combination strategy of adaptive weight particle swarm optimization (PSO) and general scale transformation stochastic resonance (GSTSR) is proposed in the paper. Firstly, in view of the self-adaptive weighted PSO featured by high precision and quick convergence, the method has made self-adaptive adjustment of systematic parameters based on PSO algorithm (with signal–noise ratio as fitness function). Secondly, GSTSR can further highlight the characteristic information otherwise covered by noise. Therefore, self-adaptive weighted PSO algorithm is combined with GSTSR to make characteristic extraction of rotor–stator rubbing faults. Finally, a comparative analysis with other methods and the analysis of faults in different states all indicate that the combination of self-adaptive weighted PSO algorithm and GSTSR can enhance rubbing fault characteristics and has effective identification of rotor–stator rubbing faults. [ABSTRACT FROM AUTHOR]

In view of the difficulty to effectively extract compound faults of rolling bearing from aero-engine and precisely identify their types, the paper has proposed a method integrating signal separation algorithm and information fusion. Firstly, the method decomposes the vibration acceleration signals collected by sensors from different positions at the same moment based on intrinsic time scale decomposition algorithm. Secondly, cross correlation analysis is given to the proper rotation component (PRC) of the same layer, which are obtained after decomposition and correspond to the sensors from different positions and cross-correlation function is introduced to embody information fusion. Thirdly, signals are reconstructed according to cross-correlation function of each PRC. Finally, based on the frequency spectrum of reconstructed signal, extract the characteristics of rolling bearing and identify the type of faults under different sensor combinations and multiple compound fault types. The result shows, the proposed method can effectively extract the characteristics of compound faults of bearing and precisely identify the type of faults under different sensor combinations and multiple compound fault types of rolling bearing. [ABSTRACT FROM AUTHOR]

To effectively identify the rotor–stator rubbing positions in aero-engine, the paper has proposed the combination of intrinsic time-scale decomposition (ITD) and classification algorithm. Regarding that with larger noise component in proper rotation component (PRC) signals after ITD, it will be more difficult to extract the characteristic information of rubbing faults, the PRC correspondings to the largest noise was eliminated. Meanwhile, signals were reconstructed based on residual proper rotation components, and positions of rubbing faults were identified according to the reconstructed signal. As rubbing extent and other factors cannot be completely the same in each rubbing, energy of reconstructed signal has been normalized to reduce the difference. Normalized energy indexes were inputted into classification algorithm as feature vectors to identify the positions of rubbing faults. To identify the superiority of approach, a comparison has been made between the proposed approach and the method of directly extracting normalized energy indexes of acceleration signals. The result of comparison shows that the two methods both work well in the identification rate of training and test samples; as for the identification rate for an unknown sample, the proposed method is superior to the other, with identification rate increasing by 17% and 9.4%. [ABSTRACT FROM AUTHOR]

Tracement of the origin of seafoods and aquatic products is an important issue for avoiding the falsification of production regions of aquatic products. Herein, by analyzing black tiger shrimp (Penaeus monodon) as a model of aquatic products, we demonstrated a method to trace the origin of aquatic products by analyzing the gut microbiome of black tiger shrimp (Penaeus monodon) collected from three commercial farms in China, via matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis. Multivariate analysis was executed on the obtained mass spectrometric datasets of bacteria extracted and collected from shrimp samples with different production regions. The gut microorganisms of black tiger shrimps can be identified by scoring the gut microbiota of black tiger shrimps collected from three producing areas. The black tiger shrimp from three production regions were found with distinguished characteristics of bacterial prevalence in their digestive gland microbiota. The dominant bacteria genus in digestive gland microbiota from black tiger shrimp samples from the three production regions are completely different. Single-blind shrimp samples with unknown production region was successfully screened by comparing the detected MS data in different ways. This strategy can trace the production region of the analyzed shrimp samples in a quick and efficient mode. The application of the present protocol was further demonstrated by analyzing the biometric species from gill tissue of Chinese mitten crab (Eriocheir sinensis) samples collected from crab-producing areas in Jiangsu Province (China), that is, Taihu Lake, Gaoyou Lake, and Hongze Lake for tracing their production area. It is expected to extend the presently developed method to provide clues for the origin traceability of other aquatic products. • Tracement of the origin of seafoods is an important issue to avoid the falsification. • A method to trace the origin of aquatic products via MALDI-TOF MS was proposed. • The dominant bacteria in digestive gland microbiota from shrimps are different. • The origin of single-blind shrimp samples can be successfully traced. • The present method holds promise for the origin traceability of aquatic products. [ABSTRACT FROM AUTHOR]

• Sensibility of variance to variation of signal intensity is used to construct Hankel matrix to highlight fault feature. • Optimal scale of the variance series was determined by sample entropy to create a multi-scale Hankel matrix. • Proposed multiscale singular value method can effectively reduce noise and highlight fault feature information. • Proposed method is insensitive to installation positions of sensors and the fault type of rolling bearings. To solve the vulnerability of singular value denoising to the effective number of singular value orders and influence of Hankel matrix structure, the paper has proposed a multiscale singular value denoising algorithm (M-SVD). Meanwhile, in concerning that when a fault of bearing occurs, the fault feature information contained in vibration signal tends to be weak and complex, the M-SVD method is blended with variational mode decomposition (VMD) algorithm in identification. Firstly, when bearing faults occur, raw vibration acceleration signals are decomposed using the superiority of VMD algorithm for non-stationary signal; in addition, considering that with the larger difference between singular values of signal, feature information will be more obvious, the method takes advantage of sensibility of variance to signal intensity and proposes to constitute Hankel matrix based on variance feature of signal. During the construction of Hankel matrix, the method to determine effective length of variance sequence (optimal scale) has been brought forward by sample entropy which is able to precisely measure the regularity of signal and detect the weak change of signal. Furthermore, component signals are denoised in line with reconstructed Hankel matrix and the spectrum of signals denoised are selected to describe fault characteristic information and judge the failure types. For the sake of verification of the accuracy and effectiveness of proposed method, the paper has analyzed and verified the data of rolling bearing in 3 different failure types and 2 different installation positions of accelerometers. The comparative analysis with classical singular value difference spectrum (SVDS) and singular value median decomposition (SVMD) denoising has further proved that presented VMD combined M-SVD method can effectively restrain noise while retaining fault characteristic information of signal. Therefore, the identification for rolling bearing fault types can be implemented, accurately. [ABSTRACT FROM AUTHOR]

Catalpol is a kind of iridoid glucoside, widely found in a variety of plants, mostly extracted from the rhizome of the traditional medicinal herb rehmanniae. It has various biological activities such as anti‐inflammatory, antioxidant, and antitumor. The anti‐inflammatory effects of catalpol have been demonstrated in a variety of diseases, such as neurological diseases, atherosclerosis, renal diseases, respiratory diseases, digestive diseases, bone and joint diseases, eye diseases, and periodontitis. The purpose of this review is to summarize the existing literature on the anti‐inflammatory effects of catalpol in a variety of inflammatory diseases over the last decade and to focus on the anti‐inflammatory mechanisms of catalpol. [ABSTRACT FROM AUTHOR]

Intravascular ultrasound (IVUS) has been frequently used for coronary artery imaging clinically. More importantly, IVUS is the fundamental image modality for most advanced multimodality intravascular imaging techniques, since it provides a more comprehensive picture of vessel anatomy on which other imaging data can be superimposed. However, image quality in the deeper region is poor because of the downgraded lateral resolution and contrast-to-noise ratio (CNR). In this paper, we report on the application of an ultrasound beamforming method that combines virtual source synthetic aperture (VSSA) focusing and coherence factor weighting (CFW) to improve the IVUS image quality. The natural focal point of conventional IVUS transducer was treated as a virtual source that emits spherical waves within a certain region. Mono-static synthetic aperture focusing was conducted to achieve higher resolution. Coherence factor was calculated using delayed RF signals and applied to the synthesized beam to increase the CNR and focusing quality. The proposed method was tested through simulations in Field II and imaging experiments in both linear and rotational scans. The lateral resolution for linear scan mode is improved from 165–524 to 126–143 \mu m ; resolution for rotational scan mode improves by up to 42%. CNR improvement by up to 1.5 was observed on the anechoic cysts of different sizes and at different locations. Herein, it is demonstrated that the beamforming method, which combines VSSA and CFW, can significantly improve the IVUS image quality. This approach can be readily integrated into the current IVUS imaging system for enhanced clinical diagnosis. [ABSTRACT FROM PUBLISHER]

Highly uniform NiCo2O4@CeO2 core@shell nanotubes with tunable CeO2 shell thicknesses have been successfully synthesized by a multi-step wet chemistry process. The use of NiCo2O4 hollow tubes coupled with CeO2 nanocrystals is the result of consideration from the view points of the components, shape and structure-effects needed for a catalyst. In the following catalytic test of CO oxidation, the as-obtained NiCo2O4@CeO2 core@shell nanocatalysts exhibit obvious component-, shape- and hybrid structure-dependent catalytic performances. Especially, the NiCo2O4@CeO2-2 sample has shown the highest activity, which makes it an ideal “noble metal-free” nanocatalyst. [ABSTRACT FROM AUTHOR]

• Take advantage of sensibility of Activity parameter to vibration intensity to describe the intensity of signals and identify different rub-impact states. • Make use of the sensitivity of Mobility parameter to signal state to determine the occurrence of rub-impact fault. • The sensitivity of Complexity parameter to signal complexity will be relied on to identify the different rub-impact states of equipment. • Hjorth parameters can be treated as the characteristic parameters for identifying of rub-impact fault and location. Hjorth parameters reveals Activity, Mobility and Complexity of signals. From this, the paper has proposed the scheme to identify a rotor–stator rub-impact fault and occurrence location by Hjorth parameters. Firstly, the approach has given an analysis to the difference of Hjorth parameters of signals and verified the sensibility of Hjorth parameters to running state of equipment; secondly, in the same running state of equipment, the consistency and robustness of Hjorth parameters of signals from the same sensor have been analyzed and proved; thirdly, for verifying the sensitivity of sensors installation locations to rub-impact fault and occurrence locations, Hjorth parameters of signals from different sensors have been analyzed; finally, the calculated Hjorth parameters are used as feature vector, and K-nearest neighbor (KNN) classification algorithm is employed for identifying a rub-impact fault and determining the rub-impact locations. The results indicate that proposed method can accurately identify the rub-impact fault and occurrence locations. [ABSTRACT FROM AUTHOR]

• Vibration signal from casing of aeroplane engine is selected. • Information fusion, ITD and Complexity parameter in Hjorth parameters is combined. • PRC associated with the least Complexity parameter in Hjorth parameters is removed. • Complexity parameter can be regarded as criterion to select effective PRCs. • Mixed faults of intershaft bearing of aeroplane engine can be correctly identified. To make correct judgment on the mixed faults of intershaft bearing of aeroplane engine, the paper has proposed a feature option method. This method works by decomposing cross-correlation function (CCF) of homogeneous information from casing with intrinsic time scale decomposition (ITD) for the separation and blending of signals. Furthermore, option of proper rotation components (PRCs) for fault identification is made by abandoning the PRC related with the least Complexity parameter in Hjorth parameters. Moreover, signal is reconstructed based on the autocorrelation function (AF) of residual PRCs. Finally, according to the frequency spectrum of signal reconstructed, fault features corresponding to intershaft bearing are extracted and fault types estimated. The result indicates that the Complexity parameter in Hjorth parameter can be used as an evaluation index to implement the selecting of sensitive fault components. Furthermore, the mixed fault types which come from intershaft bearing of aeroplane engine could be judged correctly. [ABSTRACT FROM AUTHOR]

We demonstrate a low-cost, fast and facile seeded growth method to synthesize ultra-fine Pt-porous ZnO nanoribbon hybrid materials with remarkably enhanced catalytic performances. The experimental results confirm that bio-molecule l-lysine is very important in the synthesis. Firstly, it modifies the ZnO surface, causing it to be electropositive, and secondly, it is found that it also plays the role of structure-directing agent, inducing the formation of 1D ZnO nanoribbons. For the catalytic applications of CO oxidation and the photo-degradation of organic dyes, such Pt–ZnO hybrid nanoribbons exhibit remarkably enhanced catalytic properties compared with the classical rod-like Pt–ZnO hybrids. [ABSTRACT FROM AUTHOR]

Acute coronary syndrome (ACS) is frequently associated with the sudden rupture of a vulnerable atherosclerotic plaque within the coronary artery. Several unique physiological features, including a thin fibrous cap accompanied by a necrotic lipid core, are the targeted indicators for identifying the vulnerable plaques. Intravascular ultrasound (IVUS), a catheter-based imaging technology, has been routinely performed in clinics for more than 20 years to describe the morphology of the coronary artery and guide percutaneous coronary interventions. However, conventional IVUS cannot facilitate the risk assessment of ACS because of its intrinsic limitations, such as insufficient resolution. Renovation of the IVUS technology is essentially needed to overcome the limitations and enhance the coronary artery characterization. In this paper, a multi-frequency intravascular ultrasound (IVUS) imaging system was developed by incorporating a higher frequency IVUS transducer (80 to 150 MHz) with the conventional IVUS (30?50 MHz) system. The newly developed system maintains the advantage of deeply penetrating imaging with the conventional IVUS, while offering an improved higher resolution image with IVUS at a higher frequency. The prototyped multifrequency catheter has a clinically compatible size of 0.95 mm and a favorable capability of automated image co-registration. In vitro human coronary artery imaging has demonstrated the feasibility and superiority of the multi-frequency IVUS imaging system to deliver a more comprehensive visualization of the coronary artery. This ultrasonic-only intravascular imaging technique, based on a moderate refinement of the conventional IVUS system, is not only cost-effective from the perspective of manufacturing and clinical practice, but also holds the promise of future translation into clinical benefits. [ABSTRACT FROM AUTHOR]

Ternary blend strategy has been proved to be an effective method to improve the photovoltaic performance for organic solar cells (OSCs). In this work, efficient ternary OSCs are fabricated with one donor PBDB-T and two acceptors Ph -DTDP-TIC and PC 71 BM. Ternary components can form a cascade energy level alignment when PC 71 BM as the third component to enhance charge transfer between the non-fullerene acceptor Ph -DTDP-TIC and PC 71 BM, achieving improved charge separation and collection in the ternary OSCs. Meanwhile, it demonstrated that PC 71 BM as the third component can also improve the morphology of the active layer and form suitable phase separation scale. As a results, two binary devices (PBDB-T: Ph -DTDP-TIC and PBDB-T:PC 71 BM) exhibit moderate power conversion efficiencies (PCEs) of 8.80% and 6.74%, respectively. While, the optimized PBDB-T: Ph -DTDP-TIC:PC 71 BM ternary device can produce an enhanced PCE of 11.07% through adding 40 wt% PC 71 BM in acceptors, originating from simultaneously improved J sc of 17.57 mA/cm2 and FF of 70.90%. Our work demonstrated the ternary strategy with PC 71 BM as the third component can promote charge transfer and transport to improve photovoltaic performance for OSCs. [Display omitted] • The enhanced charge transfer channel formed with dipyran acceptor as the addition of PC 71 BM. • PC 71 BM as ternary component could effectively improve blend morphology. • Ternary strategy could reach the same goal as molecular tailoring in dipyran acceptor. [ABSTRACT FROM AUTHOR]

[Display omitted] • Discarded cigarette butts were recycled to prepare activated porous carbon. • Activated carbon has a high surface area with abundant O-containing groups. • Activated porous carbons showed a removal efficiency over 90% for heavy metals. • The adsorption process of Pb2+was fast and monolayer adsorption. Adsorption capacity of CBs-derived activated carbons for Pb2+ was 249.3 mg g−1. Disposed cigarette butt is one of the biggest solid, non-biodegradable wastes across the world. Cigarette butts (CBs) made of cellulose acetate are good candidates for the initial raw materials of carbon materials synthesis, providing a possible way for waste CBs recycling. Herein a carbonaceous adsorbent derived from waste CBs was prepared for the heavy metals removal from aqueous solution. The product activated at 500 °C has a maximum adsorption capacity for lead ions, which has a favorable balance of high specific surface area and abundant O-containing functional groups, facilitating the adsorption of heavy metals ions. The adsorption experiments showed that the pH of the solution played a pivotal role in the adsorption of heavy metals by CBs-derived activated carbons and the optimal pH for the adsorption was around 5. Effective adsorption of chromium (Cr3+), cobalt (Co2+), nickel (Ni2+), lead (Pb2+) and cadmium (Cd2+) demonstrated that the prepared materials can be used for multiple heavy metal ions removal. The prepared materials were able to adsorb Pb2+ within ten minutes, and the adsorption process can be well described by the pseudo-second order model, indicating that the chemical adsorption is the rate determining step for the adsorption process. The equilibrium data followed the Langmuir isotherm model and the corresponding maximum adsorption capacity for Pb2+ was 249.3 mg g−1. Considering the merits of low cost, easy synthesis pathway and excellent removal ability of heavy metals, CBs-derived carbonaceous adsorbent exhibits potential application in the fields of heavy metal contaminated water treatment. [ABSTRACT FROM AUTHOR]

Breakthroughs in the field of nanotechnology, especially in nanochemistry and nanofabrication technologies, have been attracting much attention, and various nanomaterials have recently been developed for biomedical applications. Among these nanomaterials, nanoscale titanium dioxide (nano-TiO2) has been widely valued in stomatology due to the fact of its excellent biocompatibility, antibacterial activity, and photocatalytic activity as well as its potential use for applications such as dental implant surface modification, tissue engineering and regenerative medicine, drug delivery carrier, dental material additives, and oral tumor diagnosis and treatment. However, the biosafety of nano-TiO2 is controversial and has become a key constraint in the development of nano-TiO2 applications in stomatology. Therefore, in this review, we summarize recent research regarding the applications of nano-TiO2 in stomatology, with an emphasis on its performance characteristics in different fields, and evaluations of the biological security of nano-TiO2 applications. In addition, we discuss the challenges, prospects, and future research directions regarding applications of nano-TiO2 in stomatology that are significant and worthy of further exploration. [ABSTRACT FROM AUTHOR]

• Intrinsic Time-scale Decomposition and Graph Signal Processing is integrated. • A PR component with minimum Laplacian Energy index is chosen. • The ITD-GSP can precisely provide the characteristic frequency of compound faults. • The ITD-GSP algorithm is insensitive to the installation direction of sensors. The diagnosis of compound faults of rolling bearing is extremely difficult due to the nonlinearity and non-stationary of signal. With the purpose of extracting the compound faults from rolling bearings of aero-engine, efforts were made to integrate Intrinsic Time-scale Decomposition (ITD) and Graph Signal Processing (GSP), namely the ITD-GSP methodology. A PR component (obtained by ITD algorithm) with minimum Laplacian Energy (LE) index is chosen as the optimal rotational component to identify types of compound fault of rolling bearing. The result indicates that the proposed ITD-GSP methodology can precisely and effectively provide the characteristic frequency of compound faults no matter in which direction of sensors (vertical and horizontal) and which type of compound faults of rolling bearing. On the contrary, the mainstream schemes of fault analysis using ITD algorithm (correlation coefficients or kurtosis as the basis to screen optimal rotational component) cannot. [ABSTRACT FROM AUTHOR]

When this paper was submitted for publication (T. Ma, et al) "Multifrequency intravascular ultrasound (IVUS) imaging," IEEE Trans. Ultrason. Ferroelectr. Freq. Control, vol. 62, no. 1, pp. 97?107, Jan. 2015), c-authors Jiawen Li and Chelsea E. Munding were erroneously omitted. [ABSTRACT FROM AUTHOR]

Objective: Superharmonic contrast-enhanced ultrasound imaging, also called acoustic angiography, has previously been used for the imaging of microvasculature. This approach excites microbubble contrast agents near their resonance frequency and receives echoes at nonoverlapping superharmonic bandwidths. No integrated system currently exists could fully support this application. To fulfill this need, an integrated dual-channel transmit/receive system for superharmonic imaging was designed, built, and characterized experimentally. Method: The system was uniquely designed for superharmonic imaging and high-resolution B-mode imaging. A complete ultrasound system including a pulse generator, a data acquisition unit, and a signal processing unit were integrated into a single package. The system was controlled by a field-programmable gate array, on which multiple user-defined modes were implemented. A 6-, 35-MHz dual-frequency dual-element intravascular ultrasound transducer was designed and used for imaging. Result: The system successfully obtained high-resolution B-mode images of coronary artery ex vivo with 45-dB dynamic range. The system was capable of acquiring in vitro superharmonic images of a vasa vasorum mimicking phantom with 30-dB contrast. It could detect a contrast agent filled tissue mimicking tube of 200 μm diameter. Conclusion: For the first time, high-resolution B-mode images and superharmonic images were obtained in an intravascular phantom, made possible by the dedicated integrated system proposed. The system greatly reduced the cost and complexity of the superharmonic imaging intended for preclinical study. Significant: The system showed promise for high-contrast intravascular microvascular imaging, which may have significant importance in assessment of the vasa vasorum associated with atherosclerotic plaques. [ABSTRACT FROM PUBLISHER]

Low-level fluoride in the oral environment for a long sustained period is more effective for preventing caries. However, the current fluoride delivery methods have a short fluoride retention time and high-dose fluoride administration may increase the risk of dental fluorosis. This study developed a novel fluoride strip based poly(propylene carbonate) (PPC), which can improve oral fluoride retention for desirable anticaries effect with minimal side effects. The fluoride strips based PPC (NaF-PPC strips) with different fluoride contents (0, 1.25, 2.5 and 5 wt.%) were developed by melt-blending method. The physico-chemical characteristics, drug loading, drug release properties, remineralization and antibacterial efficacy and biocompatibility of NaF-PPC strips were investigated. The in vitro drug release studies indicated that fluoride release in a sustained manner with no initial burst release and approximately 100% of fluoride ions were released from PPC strips over 24 days. NaF-PPC strips exhibited excellent remineralization and antibacterial potential when fluoride content up to 5%. Combination with biocompatibility, 2.5% NaF-PPC strips could be a promising fluoride application for preventing caries. This work provides an effective and novel topical fluoride delivery for general use. [Display omitted] [ABSTRACT FROM AUTHOR]