An effective diagnosis method for single and multiple defects detection in gearbox based on nonlinear feature selection and kernel-based extreme learning machine

Gear transmissions have been widely used in most of today’s manufacturing and production industries; however, they often suffer from deteriorations and damages on gear pairs. Severe damages of the machinery caused by the failures of gears account for 48 %, leading to significant economic losses. Therefore it is crucial to implement fault diagnosis procedure for gearboxes. The gear meshing motion is a kind of typical strong nonlinear movement, and the related vibration signals are the nonlinear mixtures of different kinds of vibration source, leading to great difficulty in the fault feature extraction and fault detection. In order to improve the fault detection of gearboxes, a new method based on the nonlinear fault feature selection and intelligent fault identification is proposed in this work. The blind source separation (BSS) procedure was firstly employed to eliminate the influence of noise signal sources. The useful information related to the fault vibration was hence separated by the independent component analysis (ICA). Then the spectral regression (SR) was used as a nonlinear feature selection technique for the separated vibration sources. Hence, distinct fault features can be obtained. Lastly, the kernel-based extreme learning machine (KELM) was applied for the pattern recognition of single and multiply faults of the gearbox. The fault vibration data acquired from a gearbox fault experimental tester was used to valuate the proposed diagnostic method. The experiment results show that useful fault vibration signals can be separated by the new method, and the fault detection rate of the proposed method is superior to the existing approaches with an increase of 4.4 % or better. Hence, this new development will produce considerable savings by reducing unplanned outages of machinery so a company can get the full benefit from condition monitoring

Reduced Brain Activity in the Right Putamen as an Early Predictor for Treatment Response in Drug-Naive, First-Episode Schizophrenia

Antipsychotic medications can have a significant effect on brain function after only several days of treatment. It is unclear whether such an acute effect can serve as an early predictor for treatment response in schizophrenia. Thirty-two patients with drug-naive, first-episode schizophrenia and 32 healthy controls underwent resting-state functional magnetic resonance imaging. Patients were treated with olanzapine and were scanned at baseline and 1 week of treatment. Healthy controls were scanned once at baseline. Symptom severity was assessed within the patient group using the Positive and Negative Syndrome Scale (PANSS) at three time points (baseline, 1 week of treatment, and 8 weeks of treatment). The fractional amplitude of low frequency fluctuation (fALFF) and support vector regression (SVR) methods were used to analyze the data. Compared with the control group, the patient group showed increased levels of fALFF in the bilateral putamen at baseline. After 1 week of olanzapine treatment, the patient group showed decreased levels of fALFF in the right putamen relative to those at baseline. The SVR analysis found a significantly positive relationship between the reduction in fALFF after 1 week of treatment and the improvement in positive symptoms after 8 weeks of treatment (r = 0.431, p = 0.014). The present study provides evidence that early reduction and normalization of fALFF in the right putamen may serve as a predictor for treatment response in patients with schizophrenia

M3PT: A Multi-Modal Model for POI Tagging

POI tagging aims to annotate a point of interest (POI) with some informative tags, which facilitates many services related to POIs, including search, recommendation, and so on. Most of the existing solutions neglect the significance of POI images and seldom fuse the textual and visual features of POIs, resulting in suboptimal tagging performance. In this paper, we propose a novel Multi-Modal Model for POI Tagging, namely M3PT, which achieves enhanced POI tagging through fusing the target POI's textual and visual features, and the precise matching between the multi-modal representations. Specifically, we first devise a domain-adaptive image encoder (DIE) to obtain the image embeddings aligned to their gold tags' semantics. Then, in M3PT's text-image fusion module (TIF), the textual and visual representations are fully fused into the POIs' content embeddings for the subsequent matching. In addition, we adopt a contrastive learning strategy to further bridge the gap between the representations of different modalities. To evaluate the tagging models' performance, we have constructed two high-quality POI tagging datasets from the real-world business scenario of Ali Fliggy. Upon the datasets, we conducted the extensive experiments to demonstrate our model's advantage over the baselines of uni-modality and multi-modality, and verify the effectiveness of important components in M3PT, including DIE, TIF and the contrastive learning strategy.Comment: Accepted by KDD 202

Interictal Abnormalities of Neuromagnetic Gamma Oscillations in Migraine Following Negative Emotional Stimulation

Here, we aimed to investigate brain activity in migraineurs in response to emotional stimulation. Magnetoencephalography (MEG) was used to examine 20 patients with episodic migraine (EM group), 15 patients with chronic migraine (CM group), and 35 healthy participants (control group). Neuromagnetic brain activity was elicited by emotional stimulation using photographs of facial expressions. We analyzed the latency and amplitude of M100 and M170 components and used Morlet wavelet and beamformers to analyze the spectral and spatial signatures of MEG signals in gamma band (30–100 Hz). We found that the timing and frequency of MEG activity differed across the three groups in response negative emotional stimuli. First, peak M170 amplitude was significantly lower in the CM group than in the control group. Second, compared with the control group, the average spectral power was significantly lower in the EM group and CM group at M100 and M170. Third, the average spectral powers of the M100 and M170 in the CM group were negatively correlated with either HAM-D scores or migraine attack frequency. No significant differences across groups was found for positive or neutral emotional stimuli. Furthermore, after negative emotional stimuli, the MEG source analysis demonstrated that the CM group showed a significantly higher percentage of amygdala activation than the control group for M100 and M170. Thus, during headache free phases, migraineurs have abnormal brain activity in the gamma band in response to negative emotional stimuli.Trial Registration:ChiCTR-RNC-17012599. Registered 7 September, 2017

Robust Adaptive Neural Control of Morphing Aircraft with Prescribed Performance

This study proposes a low-computational composite adaptive neural control scheme for the longitudinal dynamics of a swept-back wing aircraft subject to parameter uncertainties. To efficiently release the constraint often existing in conventional neural designs, whose closed-loop stability analysis always necessitates that neural networks (NNs) be confined in the active regions, a smooth switching function is presented to conquer this issue. By integrating minimal learning parameter (MLP) technique, prescribed performance control, and a kind of smooth switching strategy into back-stepping design, a new composite switching adaptive neural prescribed performance control scheme is proposed and a new type of adaptive laws is constructed for the altitude subsystem. Compared with previous neural control scheme for flight vehicle, the remarkable feature is that the proposed controller not only achieves the prescribed performance including transient and steady property but also addresses the constraint on NN. Two comparative simulations are presented to verify the effectiveness of the proposed controller

Adaptive Neural Control Based on High Order Integral Chained Differentiator for Morphing Aircraft

This paper presents an adaptive neural control for the longitudinal dynamics of a morphing aircraft. Based on the functional decomposition, it is reasonable to decompose the longitudinal dynamics into velocity and altitude subsystems. As for the velocity subsystem, the adaptive control is proposed via dynamic inversion method using neural network. To deal with input constraints, the additional compensation system is employed to help engine recover from input saturation rapidly. The highlight is that high order integral chained differentiator is used to estimate the newly defined variables and an adaptive neural controller is designed for the altitude subsystem where only one neural network is employed to approximate the lumped uncertain nonlinearity. The altitude subsystem controller is considerably simpler than the ones based on backstepping. It is proved using Lyapunov stability theory that the proposed control law can ensure that all the tracking error converges to an arbitrarily small neighborhood around zero. Numerical simulation study demonstrates the effectiveness of the proposed strategy, during the morphing process, in spite of some uncertain system nonlinearity

Effects of oral intake fruit or fruit extract on skin aging in healthy adults: a systematic review and Meta-analysis of randomized controlled trials

BackgroundIn recent years, oral various fruits or supplements of fruits natural extracts have been reported to have significant anti-aging effects on the skin (1, 2), However, despite many studies on this topic, there is often no clear evidence to support their efficacy and safety. In this paper, we present a comprehensive review and Meta-analysis of the evidence for the safety and efficacy of oral fruits and fruits extracts in improving skin aging.MethodsFour databases, Pubmed, Embase, Web of Science, and Cochrane Library (CENTRAL), were searched for relevant literature from 2000–01 to 2023–03. Seven randomized controlled trials (RCTs) of fruit intake or fruit extracts associated with anti-skin aging were screened for Meta-analysis.ResultsCompared to placebo, oral intake of fruit or fruit extracts showed significant statistical differences in skin hydration and transepidermal water loss (TEWL), with a significant improvement in skin hydration and a significant decrease in TEWL. No significant statistical difference was observed in minimal erythema dose (MED), overall skin elasticity (R2), or wrinkle depth, and no evidence of significant improvement in skin condition was observed.ConclusionMeta-analysis results suggest that consume administration of fruits or fruit extracts significantly enhances skin hydration and reduces transcutaneous water loss, but there is insufficient evidence to support other outcome recommendations, including minimal erythema dose (MED), overall skin elasticity(R2), and wrinkle depth. Systematic Review Registration PROSPERO (york.ac.uk), identifier CRD42023410382

Osteoporosis Associated with Antipsychotic Treatment in Schizophrenia

Schizophrenia is one of the most common global mental diseases, with prevalence of 1%. Patients with schizophrenia are predisposed to diabetes, coronary heart disease, hypertension, and osteoporosis, than the normal. In comparison with the metabolic syndrome, for instance, there are little reports about osteoporosis which occurs secondary to antipsychoticinduced hyperprolactinaemia. There are extensive recent works of literature indicating that osteoporosis is associated with schizophrenia particularly in patients under psychotropic medication therapy. As osteoporotic fractures cause significantly increased morbidity and mortality, it is quite necessary to raise the awareness and understanding of the impact of antipsychoticinduced hyperprolactinaemia on physical health in schizophrenia. In this paper, we will review the relationship between schizophrenia, antipsychotic medication, hyperprolactinaemia, and osteoporosis

Osteoporosis Associated with Antipsychotic Treatment in Schizophrenia

Schizophrenia is one of the most common global mental diseases, with prevalence of 1%. Patients with schizophrenia are predisposed to diabetes, coronary heart disease, hypertension, and osteoporosis, than the normal. In comparison with the metabolic syndrome, for instance, there are little reports about osteoporosis which occurs secondary to antipsychotic-induced hyperprolactinaemia. There are extensive recent works of literature indicating that osteoporosis is associated with schizophrenia particularly in patients under psychotropic medication therapy. As osteoporotic fractures cause significantly increased morbidity and mortality, it is quite necessary to raise the awareness and understanding of the impact of antipsychotic-induced hyperprolactinaemia on physical health in schizophrenia. In this paper, we will review the relationship between schizophrenia, antipsychotic medication, hyperprolactinaemia, and osteoporosis