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Fault diagnosis of rolling bearings under variable speed conditions is a challenging task since the vibration signal exhibits time-varying non-stationary characteristics and is usually contaminated by strong noise. Most of the current researches employ the adaptive filtering or signal decomposition methods to obtain the impulse signals caused by the bearing fault before feature extraction, which, however, are not capable of removing the in-band noise. To address this issue, a novel method called non-stationary harmonic summation (NHS) is proposed based on the fact that the repetitive impulses caused by the bearing fault consist of a series of equally-spaced harmonics in the frequency domain. Firstly, the harmonic characteristics are theoretically analyzed and the results show that the impulses contain non-stationary harmonics with a time-varying spacing frequency (i.e., the fault characteristic frequency) under variable speed conditions. Next, according to the harmonic characteristics, an efficient algorithm combining the parameterized demodulation with the adaptive chirp mode decomposition is developed to extract the non-stationary harmonics and then summate these harmonics to reconstruct the repetitive impulses for the fault feature extraction. Since the NHS elaborately exploits the intrinsic harmonic structure of the impulse signals, the noise can be fully removed and the reconstructed signal is free of side-band interference caused by complex amplitude modulation. Both simulated and experimental signals are considered to demonstrate the advantages of the NHS for bearing fault diagnosis under variable speed conditions.

Wheel polygonal wear is a common and severe defect, which seriously threatens the running safety and reliability of a railway vehicle especially a locomotive. Due to non-stationary running conditions (e.g., traction and braking) of the locomotive, the passing frequencies of a polygonal wheel will exhibit time-varying behaviors, which makes it too difficult to effectively detect the wheel defect. Moreover, most existing methods only achieve qualitative fault diagnosis and they cannot accurately identify defect levels. To address these issues, this paper reports a novel quantitative method for fault detection of wheel polygonization under non-stationary conditions based on a recently proposed adaptive chirp mode decomposition (ACMD) approach. Firstly, a coarse-to-fine method based on the time–frequency ridge detection and ACMD is developed to accurately estimate a time-varying gear meshing frequency and thus obtain a wheel rotating frequency from a vibration acceleration signal of a motor. After the rotating frequency is obtained, signal resampling and order analysis techniques are applied to an acceleration signal of an axle box to identify harmonic orders related to polygonal wear. Finally, the ACMD is combined with an inertial algorithm to estimate polygonal wear amplitudes. Not only a dynamics simulation but a field test was carried out to show that the proposed method can effectively detect both harmonic orders and their amplitudes of the wheel polygonization under non-stationary conditions.

The incretin receptors, glucagon-like peptide-1 receptor (GLP-1R) and glucose-dependent insulinotropic polypeptide receptor (GIPR), are prime therapeutic targets for the treatment of type 2 diabetes (T2D) and obesity. They are expressed in pancreatic beta cells where they potentiate insulin release in response to food intake. Despite GIP being the main incretin in healthy individuals, GLP-1R has been favored as a therapeutic target due to blunted GIPR responses in T2D patients and conflicting effects of GIPR agonists and antagonists in improving glucose tolerance and preventing weight gain. There is, however, a recently renewed interest in GIPR biology, following the realization that GIPR responses can be restored after an initial period of blood glucose normalization and the recent development of dual GLP-1R/GIPR agonists with superior capacity for controlling blood glucose levels and weight. The importance of GLP-1R trafficking and subcellular signaling in the control of receptor outputs is well established, but little is known about the pattern of spatiotemporal signaling from the GIPR in beta cells. Here, we have directly compared surface expression, trafficking, and signaling characteristics of both incretin receptors in pancreatic beta cells to identify potential differences that might underlie distinct pharmacological responses associated with each receptor. Our results indicate increased cell surface levels, internalization, degradation, and endosomal vs plasma membrane activity for the GLP-1R, while the GIPR is instead associated with increased plasma membrane recycling, reduced desensitization, and enhanced downstream signal amplification. These differences might have potential implications for the capacity of each incretin receptor to control beta cell function.

Irregularities of rail joints ordinarily induce high-frequency impacts on wheel–rail contact systems, which may further influence vehicle–track interaction. In this study, a three-dimensional locom...

Aiming at preventing frequently occurred coupler jack-knifing in slave control locomotives of 20,000-tonne heavy-haul trains, a theoretical and experimental research on enhancing the coupler stabil...

The incretin receptors, glucagon-like peptide-1 receptor (GLP-1R) and glucose-dependent insulinotropic polypeptide receptor (GIPR), are class B GPCRs and prime therapeutic targets for the treatment of type 2 diabetes (T2D) and obesity. They are expressed in pancreatic beta cells where they potentiate insulin release in response to food intake. Despite GIP being the main incretin in healthy individuals, GLP-1R has been favoured versus GIPR as a therapeutic target due to GIPR responses being blunted in T2D patients and the conflicting effects of GIPR agonists and antagonists in improving glucose tolerance and preventing weight gain. There is, however, a recently renewed interest in GIPR biology following the realisation that GIPR responses can be restored after an initial period of blood glucose normalization and the recent development of dual GLP-1R-GIPR agonists with superior capacity for the control of blood glucose levels and weight. The importance of GLP-1R trafficking and subcellular signaling in the control of receptor outputs is well established, but little is known about the pattern of spatiotemporal signaling from the GIPR in beta cells. Here we have directly compared the main trafficking and signaling characteristics of both receptors in pancreatic beta cells, finding striking differences in their propensities for internalization, recycling, and degradation, as well as plasma membrane versus endosomal activity, with potential implications for receptor-specific control of beta cell function.

Background: Type 2 diabetes (T2D) is characterised by the loss of pulsatile insulin secretion. We studied mice with β-cell specific loss of the glucagon receptor (GCGRflox/flox X Ins-1 cre) , to investigate the role of intra-islet glucagon signalling on pan-islet calcium oscillations and islet pulsatility. Methods: Frequently sampled intravenous glucose tolerance tests were conducted on GCGRKOβ-cells-/-and littermate controls. Crossing with GCaMP6f (STOP flox) animals allowed for β-cell specific expression of a fluorescent calcium indicator. These islets were functionally imaged in vitro and in vivo. Wild-type mice were transplanted with islets expressing GCaMP6f in β-cells into the anterior eye chamber and placed on a high fat diet. Half of the cohort received a glucagon analogue (GCG-analogue) for 40 days and the control group were fed to achieve weight matching. Calcium imaging was performed regularly during the development of hyperglycaemia and in response to GCG-analogue treatment. Results: GCGRKOβ-cells-/- mice had poorer glucose tolerance in an intraperitoneal glucose tolerance test (control 12.7mmol/L ±0.6 vs. GCGRKOβ-cells-/- 15.4mmol/L ±0.0 at 15 min, p=0.002) ; fasting glycaemia was not different to controls. In vitro, GCGRKOβ-cells-/- islets showed profound loss of synchronised calcium waves in response to glucose which is only partially rescued in vivo. First-phase insulin pulsatility on peripheral blood sampling (n=5) was significantly disordered in GCGRKOβ-cells-/- mice (burst mass GCGRKOβ-cells-/- 0.30 ±0.03 versus 0.84 ±0.23 for controls p=0.04) . Diet induced obesity and hyperglycaemia resulted in a loss of co-ordinated calcium waves in transplanted islets. This was reversed with GCG-analogue treatment, independently of weight-loss (n=8) . Conclusion: These data provide novel evidence for the role of intra-islet glucagon in sustaining synchronised calcium oscillations and support a possible therapeutic role for glucagonergic agents to restore the insulin pulsatility lost in T2D. Disclosure K.Suba: n/a. B.Jones: None. T.M.Tan: Consultant; Zihipp Ltd. B.Owen: None. D.J.Drucker: Consultant; Kallyope, Merck Sharp & Dohme Corp., Novo Nordisk, Pfizer Inc., Other Relationship; Eli Lilly and Company. S.Bloom: None. K.Murphy: None. V.Salem: None. Y.S.Patel: Employee; PerkinElmer. A.Roberts: None. J.V.Shrewsbury: None. S.Chen: None. R.Kwok: None. V.Kalogianni: None. X.Liu: None. G.A.Rutter: Advisory Panel; Sun Pharmaceutical Industries Ltd., Research Support; Sun Pharmaceutical Industries Ltd.

CO2 reduction catalysis plays an important role in the process of converting harmful exhaust gas into useful fuels. However, the product complexity and the difficult hydrogenation in critical steps make it difficult to find a suitable catalyst for CO2 reduction. In this work, we report homo/hetero bimetal embedded in two-dimensional materials for electrocatalysis and discovered a new descriptor. We chose β12-borophene accommodating two transition metal atoms for efficient CO2RR as a model system. We found that MnCo and VV systems are promising for CO2 reduction with good stability and high selectivity over HER. Through least absolute shrinkage and selection operator (LASSO) regression, we discovered a new integrated descriptor containing the spin moment of the metals and the descriptor is linked with the performance of the first step of CO2 hydrogenation. The MnCo system could catalyze a C1 process with low free energy change of the rate determining step. The VV system could also conduct the C2 process with low free energy change of the rate determining step. Bader charge analysis shows the ability of the borophene substrate to provide or hold electrons. This work demonstrates homonuclear and heteronuclear biatomic catalysts with high activity for CO2RR.

In the field of vibration measurement, the vision-based approach can achieve high spatial resolution compared to traditional accelerometers and lasers and thus has attracted much attention during the past decades. A recently developed phase-based video motion magnification (PVMM) technique to estimate subtle motions from videos is only suitable for stationary vibration measurement, because it is limited to analyze the well-separated vibration modes. In this article, a time-varying motion filtering (TVMF) method is presented to endow the PVMM technique with the ability to decompose nonstationary vibrations and visualize the time-dependent mode shapes of nonstationary systems. Specifically, we first build a parameterized mathematical model for the video motions and then extract each vibration mode by optimizing a set of parameters. After modulating each mode with an amplification factor, the time-varying characteristics of the vibration mode shapes can be visualized to naked eyes. Compared to the traditional PVMM technique, the merit of the proposed TVMF is verified to be able to produce less noise and fewer artifacts in nonstationary vibration measurement. The performance of TVMF is demonstrated on both a simulated experiment and a real nonstationary moving mass system.

Recently, the reduction of CO2 to fuels has been the subject of numerous studies, but the selectivity and activity remain inadequate. Progress has been made on single-site two-dimensional catalysts...

Tread wear rates of the right and left wheels of a wheelset are not the same because of the complexity of the track condition, which causes the wheel diameter difference (WDD). The WDD can influence vehicle dynamic performances and shorten the service life of the wheelset. To diagnose and recognize the condition of the WDD in time, a data-driven method based on multi-sensor information fusion is proposed. Different statistical features are extracted from the time and frequency domains of the axle-box acceleration signals. The features can be fused by integrating stacked autoencoder and multiple kernel learning. The comparative experimental analysis shows that compared with other commonly used intelligent methods, the proposed method can achieve higher diagnostic accuracy and give better performance with small training sample sizes. The statistical features sensitive to the WDD are also analyzed for industrial application.

CO

Due to the significant cognition reduction, multi-media content has become an increasingly important information type nowadays. More and more descriptions are coupled with images to make them more attractive and persuasive. Currently, several text-image retrieval methods have been developed to improve the efficiency of the time-consuming and professional process. However, in practical retrieval applications, it is the vivid and terse descriptions that are widely used, instead of the shallow captions that describe what is contained. Therefore, the most existing methods designed for the caption-style text can not achieve this purpose. To eliminate the mismatch, we introduce a novel problem about description-image retrieval and propose the specially designed method, named Adapted Graph Reasoning and Filtration (AGRF). In AGRF, we firstly leverage an adapted graph reasoning network to discover the combination of visual objects in the image. Then, a cross-modal gate mechanism is proposed to cast aside those description-independent combinations. Experiment results on the real-world dataset demonstrate the advantages of the AGRF over the state-of-the-art methods.

Modal parameter identification is a mature technology. However, there are some challenges in its practical applications such as the identification of vibration systems involving closely spaced modes and intensive noise contamination. This paper proposes a new time-frequency method based on intrinsic chirp component decomposition (ICCD) to address these issues. In this method, a redundant Fourier model is used to ameliorate border distortions and improve the accuracy of signal reconstruction. The effectiveness and accuracy of the proposed method are illustrated using three examples: a cantilever beam structure with intensive noise contamination or environmental interference, a four-degree-of-freedom structure with two closely spaced modes, and an impact test on a cantilever rectangular plate. By comparison with the identification method based on the empirical wavelet transform (EWT), it is shown that the presented method is effective, even in a high-noise environment, and the dynamic characteristics of closely spaced modes are accurately determined.

Time-varying systems are applied extensively in practical applications, and their related parameter identification techniques are of great significance for structural health monitoring of time-varying systems. To improve the identification accuracy for time-varying systems, this study puts forward a novel parameter identification approach in the time–frequency domain using intrinsic chirp component decomposition (ICCD). ICCD is a powerful tool for signal decomposition and parameter extraction, with good signal reconstruction capability in a high-noise environment. To maintain good identification effects for the time-varying system in a noisy environment, the proposed method introduces a redundant Fourier model for the non-stationary signal, including instantaneous frequency (IF) and instantaneous amplitude (IA). The accuracy and effectiveness of the proposed approach are demonstrated by a single-degree-of-freedom system with three types of time-varying parameters, as well as an example of a multi-degree-of-freedom system. The effects of different levels of measured noise on the identified results are also discussed in detail. Numerical results show that the proposed method is very effective in tracking the smooth, periodical, and non-smooth variations of time-varying systems over the entire identification time period even when the response signal is contaminated by intense noise.

Recently, a powerful signal processing technique called variational mode decomposition (VMD) has drawn a great interest in various applications. The VMD assumes that the signal components are narrowly banded and separated in the frequency domain. Therefore, it cannot analyze wideband nonstationary signals with spectrum-overlapped components. To address this issue, a novel scheme called warped VMD (WVMD) is proposed in this paper. The WVMD includes the following steps. First, the phase function of a certain signal component is accurately estimated by optimizing a spectrum concentration index. Next, the time-warping principle is used to transform the original signal to a warped one in the phase coordinate where the spectrums of the components are well separated. Then, the conventional VMD can be used to effectively decompose the warped signal. Finally, the obtained signal components (of the warped signal) can be taken back to the time coordinate by the inverse warping operation. The WVMD works well in noisy environments and can even decompose signals with very close components. The effectiveness of the WVMD is validated by some simulated signals, a bat echolocation signal, and applications to real-life vibration signals of a rotor test rig and a hydroturbine.

The rub-impact fault occurring in the rotating machinery can cause the periodically frequency modulation (FM) in its vibration response. Such kind of modulation mode has been named as intra-wave FM effect, which carries the intrinsic fault information of rubbing-related dynamic system. Assisted by a fast-modulated instantaneous frequency (IF) extraction method named M-VNCMD, the intra-wave FM phenomena arising in several simulated and real rubbing responses are revealed and investigated in detail in this paper. To be specific, the intra-wave FM response is firstly modeled as the sinusoidal modulation form according to the extracted fault features, and then expanded as a series of harmonics to establish the relationship between its modulation pattern and Fourier spectrum. The connection among the fault diagnosis criteria based on modulation patterns and Fourier spectrum is finally uncovered to demonstrate the superiority of diagnosing the rubbing fault depending on modulation features.

For rotating machineries, rub-impact between the surfaces of the rotor and the stator during the rotation is a common and severe fault. The rub-impact fault will lead to a frequency-modulated (FM) vibration signal with a fast fluctuating instantaneous frequency (IF). It is difficult to extract the fast fluctuating IF feature due to the limited resolution of the current time-frequency (TF) methods. The recently proposed variational nonlinear chirp mode decomposition (VNCMD) method shows promising advantages in analyzing strongly FM signals. However, the VNCMD solves a joint-estimation problem which requires the prior information of the number of the signal components and may cause instability issues. In this paper, a tractable version of the VNCMD, called adaptive chirp mode decomposition (ACMD), is introduced to extract the fast fluctuating IF of the vibration signal from the rub-impact rotor. The ACMD employs a greedy algorithm to catch each signal component individually. Moreover, using the estimated instantaneous amplitude and the IF by ACMD, we obtain a high-resolution adaptive TF spectrum which can clearly represent the rub-impact feature of the vibration signal. The effectiveness of the fault detection method based on ACMD is demonstrated by dynamic simulations at first. Then, the method is applied to vibration signals of a heavy oil catalytic cracking machine set indicating its usefulness in early fault detection and multi-feature extraction.

Signal decomposition has drawn growing interest in various applications these days. Some recent decomposition methods, like the variational mode decomposition (VMD) and the variational nonlinear chirp mode decomposition (VNCMD), employ a joint-optimization scheme to accurately estimate all the signal modes underlying a signal. Some existing issues for these methods are: requiring prior knowledge of the number of the signal modes, empirically setting the bandwidth parameter to a fixed value, and lacking of an effective initialization scheme for the optimization algorithm. To address these issues, this paper presents a new decomposition approach called adaptive chirp mode pursuit (ACMP). Similar to the matching pursuit method, the ACMP captures signal modes one by one in a recursive framework. In addition, an adaptive bandwidth parameter updating rule and an instantaneous frequency initialization method based on Hilbert transform are incorporated into the ACMP. Several examples including simulated signals as well as real-life applications are provided to show the effectiveness and advantages of the ACMP.

Signals propagated in dispersive systems usually present multimodal and nonlinear group delay (GD) properties. The characterization of GDs and the signal decomposition are still challenging issues. Existing methods are mainly limited to identify monotonous GDs by estimating monotonous instantaneous frequencies. Bandpass filters are usually utilized to separate each mode, but the inherent defects of digital filters will decrease the reconstruction accuracy inevitably. In this paper, we propose the frequency-domain intrinsic component decomposition method (FICD). On one hand, it can characterize nonlinear and non-monotonic GDs in frequency domain by using different kernel functions. On the other hand, the method itself can act as a time-frequency filter to separate and reconstruct each mode simultaneously. One of the advantages of our method is that it can deal with close or overlapped signals with high precision. Finally, simulated examples are provided to verify the effectiveness and efficiency.

The blind source separation (BSS) concerns recovering sources from their mixtures. Specifically, the sources in this paper, named intrinsic chirp sources (ICSs), are modeled as the linear combination of non-linear chirp components (NCCs). A novel method is developed here to address the blind separation issue of them. Firstly, all the mixtures at each channel are decomposed into a series of NCCs by a parameterized decomposition approach. It can adapt to NCCs with time-frequency (T-F) distribution suffering from bad T-F concentration and non-disjoint T-F overlapping. Next, the reconstructed NCCs can be clustered into corresponding ICS according to the fact that the NCCs belonging to the identical ICS share the same column in mixing matrix. The source recovery and mixing matrix estimation are finally accomplished based on the clustering consequence. Three simulations demonstrate the capability of our method in dealing with challenging under-determined BSS cases and its potential in practical applications.

The coupler is an essential component on the train that has the function of connecting and buffering. The actual dynamic performance of the coupler directly influences the safety and comfort of the vehicle. When the heavy haul train passes through the curve, the extreme swing angles of the couplers will seriously threaten the safety of the train. Therefore, the kernelized correlation filter-template matching (KCF-Match) target tracking algorithm is proposed to track the position and calculate the swing angles of the couplers. After the tracked area is selected, the corresponding data of the area are input into the KCF target tracking model for tracking. During the tracking process, if the tracking effects are not satisfied with the given evaluation indexes, the template matching algorithm will be used to track again. Experiments show that KCF-Match target tracking algorithm can achieve 99.8% accuracy rate and 99.9% success rate on the premise of ensuring real-time performance.

The glucagon-like peptide-1 receptor (GLP-1R) is an important regulator of glucose homeostasis and has been successfully targeted for the treatment of type 2 diabetes. Recently described biased GLP-1R agonists with selective reductions in β-arrestin versus G protein coupling show improved metabolic actions in vivo. However, two prototypical G protein-favouring GLP-1R agonists, P5 and exendin-F1, are reported to show divergent effects on insulin secretion. In this study we aimed to resolve this discrepancy by performing a side-by-side characterisation of these two ligands across a variety of in vitro and in vivo assays. Exendin-F1 showed reduced acute efficacy versus P5 for several readouts, including recruitment of mini-G proteins, G protein-coupled receptor kinases (GRKs) and β-arrestin-2. Maximal responses were also lower for both GLP-1R internalisation and the presence of active GLP-1R-mini-Gs complexes in early endosomes with exendin-F1 treatment. In contrast, prolonged insulin secretion in vitro and sustained anti-hyperglycaemic efficacy in mice were both greater with exendin-F1 than with P5. We conclude that the particularly low acute efficacy of exendin-F1 and associated reductions in GLP-1R downregulation appear to be more important than preservation of endosomal signalling to allow sustained insulin secretion responses. This has implications for the ongoing development of affinity- versus efficacy-driven biased GLP-1R agonists as treatments for metabolic disease.

Real-time chatter detection is important in improving the surface quality of workpieces in milling. Since the process from stable cutting to chatter is characterized by the progressive variation of the vibration energy distribution, entropy has been utilized to capture the decreasing randomness of vibration signals when chatter occurs. To make such an index more sensitive to transitions of the cutting state, the entropy can be computed based on signal components obtained through signal decomposition techniques. However, the classic empirical mode decomposition (EMD) is difficult to put into practice due to its weak robustness to noises. The up-to-date variational mode decomposition (VMD) has strict requirements on priori information of the signal and thus is not applicable either. In this paper, a novel method named the iterative Vold-Kalman filter (I-VKF) is proposed under the framework of the greedy algorithm, where the Vold-Kalman filter (VKF), a classic order-tracker for rotating machinery, is improved to recursively extract each signal component. In the meantime, a spectrum concentration index-based technique is developed for the instantaneous chatter frequency estimation to adaptively determine the filter parameter. Numerical examples demonstrate the superiority of the I-VKF over the original VKF, EMD, and VMD, especially in the presence of strong noises. Combined with the energy entropy of extracted components and an automatically calculated threshold, the proposed strategy greatly helps in timely chatter detection, which has been verified by dynamic simulation and experiments.

The glucagon-like peptide-1 receptor (GLP-1R) is an important regulator of blood glucose homeostasis. Ligand-specific differences in membrane trafficking of the GLP-1R influence its signalling properties and therapeutic potential in type 2 diabetes. Here, we have evaluated how different factors combine to control the post-endocytic trafficking of GLP-1R to recycling versus degradative pathways. Experiments were performed in primary islet cells, INS-1 832/3 clonal beta cells and HEK293 cells, using biorthogonal labelling of GLP-1R to determine its localisation and degradation after treatment with GLP-1, exendin-4 and several further GLP-1R agonist peptides. We also characterised the effect of a rare GLP1R coding variant, T149M, and the role of endosomal peptidase endothelin-converting enzyme-1 (ECE-1), in GLP1R trafficking. Our data reveal how treatment with GLP-1 versus exendin-4 is associated with preferential GLP-1R targeting towards a recycling pathway. GLP-1, but not exendin-4, is a substrate for ECE-1, and the resultant propensity to intra-endosomal degradation, in conjunction with differences in binding affinity, contributes to alterations in GLP-1R trafficking behaviours and degradation. The T149M GLP-1R variant shows reduced signalling and internalisation responses, which is likely to be due to disruption of the cytoplasmic region that couples to intracellular effectors. These observations provide insights into how ligand- and genotype-specific factors can influence GLP-1R trafficking. Published version

Recently, the reduction of CO

Two-dimensional van der Waals heterostructure materials, particularly transition metal dichalcogenides (TMDC), have proved to be excellent photoabsorbers for solar radiation, but performance for such electrocatalysis processes as water splitting to form H2 and O2 is not adequate. We propose that dramatically improved performance may be achieved by combining two independent TMDC while optimizing such descriptors as rotational angle, bond length, distance between layers, and the ratio of the bandgaps of two component materials. In this paper we apply the least absolute shrinkage and selection operator (LASSO) process of artificial intelligence incorporating these descriptors together with quantum mechanics (density functional theory) to predict novel structures with predicted superior performance. Our predicted best system is MoTe2/WTe2 with a rotation of 300°, which is predicted to have an overpotential of 0.03 V for HER and 0.17 V for OER, dramatically improved over current electrocatalysts for water splitting.

Periodical impulses detection is the key issue for vibration-based condition monitoring and fault diagnosis of rotating machinery. Inspired from spectral distribution of faulty vibration signal, a novel harmonic-decomposition based periodical impulses detection method is proposed in this paper and applied in rub-impact fault diagnosis for rotor system. Firstly, the resonance band where the impulses implied in sensed vibration signal locate, is identified by the fast kurtogram algorithm. It is demonstrated that the fault-stimulated periodical impulses can be expanded as a series of harmonics equipped with a uniform frequency interval in spectral distribution. An adaptive harmonic-decomposition method is thus developed to decompose the harmonics within the identified band with periodical impulses reconstructed. The rubbing fault diagnosis for rotor system of rotating machinery is finally achieved according to the envelope analysis for reconstructed periodical impulses. Compared with detecting results obtained by band-pass filtering based approach, the proposed method is able to detect the periodical impulses with higher reconstructed signal-to-noise (SNR) and more distinct fault features extracted, which is very helpful to reach a reliable diagnosis conclusion.

Signal bias and membrane trafficking have recently emerged as important considerations in the therapeutic targeting of the glucagon-like peptide-1 receptor (GLP-1R) in type 2 diabetes and obesity. In the present study, we have evaluated a peptide series with varying sequence homology between native GLP-1 and exendin-4, the archetypal ligands on which approved GLP-1R agonists are based. We find notable differences in agonist-mediated signalling, endocytosis and recycling, dependent both on the introduction of a His → Phe switch at position 1 and the specific mid-peptide helical regions and C-termini of the two agonists. These observations were linked to insulin secretion in a beta cell model and provide insights into how ligand factors influence GLP-1R function at the cellular level.Graphical abstract

Signal denoising is crucial to condition monitoring of mechanical systems as fault characteristics may be hidden by strong noise in some cases. In this paper, we focus on the denoising of nonstationary vibration signals which contain multiple complex components. To this end, we introduce a time-frequency filter called envelope-tracking filter (ETF). The basic idea of the ETF is to characterize the envelope of a signal using Fourier basis functions and estimate the envelope by solving a least-squares problem. We show that the ETF is similar to the Vold–Kalman filter, but it has several promising advantages, i.e., easier to determine the bandwidth expression, more flexible to handle signals at different noise levels, and less sensitive to errors of instantaneous frequency (IF) estimations. Our denoising method includes two steps: 1) extract IFs of each nonstationary component by optimizing a spectrum concentration index, and 2) recover each component by using the ETF. Our method works very well in heavy noise and is effective for signals with overlapped components. Some examples including simulated and real-life vibration signals are offered to show the validity of the method.

Doppler frequency can reflect the dynamic properties of the radar target and thus provides an effective approach to analyze the target signatures. In this paper, a novel method is proposed to accurately estimate Doppler frequencies of multi-component radar signals. The method employs a more general model, i.e., the redundant Fourier model to represent various Doppler frequency trajectories. In the first stage of the method, a multi-component signal is decomposed into some individual components using a de-chirping method. Then, the parameterized time-frequency transform (PTFT) with a matched Fourier kernel is designed to estimate frequencies of the obtained components. With the estimated frequencies, the complex envelopes of the components can then be estimated through a joint-least-squares method. We show that the phase information of the obtained envelopes can be used for error compensation in the frequency estimation. The proposed method integrating the PTFT with the phase compensation technique can significantly improve the accuracy of Doppler frequency estimation. Both simulated and experimental examples are provided, indicating the potential of the method in analyzing Doppler signals with overlapped components. The future study will focus on the real applications of the method for radar target detection and monitoring, such as human vital sign detection and condition monitoring of industrial equipments (e.g., wind turbines).

As a specific case of chirp component, the short-time chirp component (STCC) only continues a duration in a reference interval with a birth and death. A STCC usually possesses of important information directly related to kinematic or structural characteristics of a target. In this paper, a novel method is developed to decompose a multi-component short-time chirp signal into a series of STCCs. The decomposition method is mainly based on a parameterized mathematical model, which is used to represent the STCC, and a peak tracking approach, which is adopted to extract the instantaneous frequencies (IFs), and corresponding births and deaths for all the STCCs. With the model and the extracted IFs, the decomposition can be conducted by solving a linear system of equations. The effectiveness of the proposed method is demonstrated by two simulated examples and a marine biologic sound.

This paper presents an analytical study on evaluation of support loss in micromechanical resonators undergoing in-plane flexural vibrations. Two-dimensional elastic wave theory is used to determine the energy transmission from the vibrating resonator to the support. Fourier transform and Green's function technique are adopted to solve the problem of wave motions on the surface of the support excited by the forces transmitted by the resonator onto the support. Analytical expressions of support loss in terms of quality factor, taking into account distributed normal stress and shear stress in the attachment region, and coupling between the normal stress and shear stress as well as material disparity between the support and the resonator, have been derived. Effects of geometry of micro-beam resonators, and material dissimilarity between support and resonator on support loss are examined. Numerical results show that ‘harder resonator’ and ‘softer support’ combination leads to larger support loss. In addition, the Perfectly Matched Layer (PML) numerical simulation technique is employed for validation of the proposed analytical model. Comparing with results of quality factor obtained by PML technique, we find that the present model agrees well with the results of PML technique and the pure-shear model overestimates support loss noticeably, especially for resonators with small aspect ratio and large material dissimilarity between the support and resonator.

Graphical abstract, The glucagon-like peptide-1 receptor (GLP-1R) is an important regulator of glucose homeostasis and has been successfully targeted for the treatment of type 2 diabetes. Recently described biased GLP-1R agonists with selective reductions in β-arrestin versus G protein coupling show improved metabolic actions in vivo. However, two prototypical G protein-favouring GLP-1R agonists, P5 and exendin-F1, are reported to show divergent effects on insulin secretion. In this study we aimed to resolve this discrepancy by performing a side-by-side characterisation of these two ligands across a variety of in vitro and in vivo assays. Exendin-F1 showed reduced acute efficacy versus P5 for several readouts, including recruitment of mini-G proteins, G protein-coupled receptor kinases (GRKs) and β-arrestin-2. Maximal responses were also lower for both GLP-1R internalisation and the presence of active GLP-1R-mini-Gs complexes in early endosomes with exendin-F1 treatment. In contrast, prolonged insulin secretion in vitro and sustained anti-hyperglycaemic efficacy in mice were both greater with exendin-F1 than with P5. We conclude that the particularly low acute efficacy of exendin-F1 and associated reductions in GLP-1R downregulation appear to be more important than preservation of endosomal signalling to allow sustained insulin secretion responses. This has implications for the ongoing development of affinity- versus efficacy-driven biased GLP-1R agonists as treatments for metabolic disease.

Vibration signals related to planetary gearbox faults under non-stationary conditions will be equipped with sinusoidal modulation laws in their amplitude modulation (AM) and frequency modulation (FM) modes. Extracting fault features implied in the AM and FM modes is the key issue to detect inner gear faults occurring in planetary gearboxes. However, there exist few signal analysis approaches that are able to track the fast oscillating nature implied in the fault-induced sinusoidal FM patterns. To address such a challenging issue, this paper conducts a detailed study on sinusoidal FM patterns of the fault-related vibration signal model and proposes a novel method to extract them successfully. To be specific, the proposed method firstly separates the AM and FM modes via Hilbert transform to avoid their mutual interference. The intrinsic modulation features implied in the AM and FM modes can then be separately extracted by a developed signal decomposition method named iterative-joint chirp mode decomposition (I-JCMD) with a composite mode optimization scheme. Compared with the existing fault detection techniques, the proposed method is not only able to avoid the complicated sideband analysis but also identify the fast and time-varying oscillating nature of the FM mode with a high accuracy. The performance of our method is finally verified by three simulated cases and four groups of real vibration signals related to four types of sun gear faults occurring in a planetary gearbox test-rig, which also reveals a potential scaling relation between load strength and the oscillating coefficient of the FM mode.