Channel Signal Research Articles

Model-Assisted Muti-source Fusion Hypergraph Convolutional Neural Networks for intelligent few-shot fault diagnosis to Electro-Hydrostatic Actuator

Electro-Hydrostatic Actuator (EHA) is a critical electro-hydraulic actuator system widely used in aerospace equipment. To ensure its normal operation, the intelligent fault diagnosis of the EHA system has gained increasing attention. However, the EHA exhibits strong nonlinearity, high structural complexity, and difficulty obtaining fault samples. A Model-Assisted Multi-source Fusion Hypergraph Convolutional Neural Network (MAMF-HGCN) is proposed to address the few-shot intelligent fault diagnosis of EHA. Specifically, the fault data obtained from the hydraulic simulation model is used to establish the relationship among each channel signal. This assists in constructing a hypergraph structure for actual multi-source fault data under limited samples. Each node in the hypergraph employs message transmission to fuse signals from different channels. Subsequently, the hypergraph data are input into the constructed hypergraph convolutional neural network to perform fault classification. Finally, validating the EHA hydraulic system test rig from Nanjing University of Science and Technology illustrates the method’s effectiveness in diagnosing hydraulic system problems under limited fault samples.

A Weighted Adaptive Range-Averaging Technique to Improve the Precision Consistency of Polarimetric Variable Fields

Abstract The two main metrics for the performance evaluation of radar-variable estimators are the bias and standard deviation (SD) of estimates. Depending on the estimator properties, the bias may increase as the signal-to-noise ratio (SNR) decreases. The standard deviation, however, always rises as the SNR becomes smaller. For instance, if estimates are computed from 16 samples (typically used for WSR-88D surveillance scans) using a rectangular data window and the maximum unambiguous velocity is ∼9 m s−1, the standard deviation of reflectivity estimates increases 1.6 times as the SNR drops from 20 to 2 dB. But for estimates of differential reflectivity, differential phase, and copolar correlation coefficient, SDs increase ∼6.7, ∼6, and ∼54 times, respectively. Hence, this effect impacts the polarimetric variables substantially more than the spectral moments. Additionally, the polarimetric variable SD is also sensitive to the correlation between signals in horizontal and vertical channels leading to reduced data quality in the regions where the correlation coefficient is low. Such increases in the variability of estimates are observable in the fields of dual polarization variables as an increased spatial inhomogeneity (or noisiness) in the areas where radar echoes exhibit low-to-moderate SNRs and/or decreased correlation coefficient. These effects can obscure the visual identification of weather features as well as adversely impact algorithms. Herein, a novel method that applies variable smoothing in the range where the smoothing intensity depends on the SDs of estimates is presented. It applies little or no range averaging in the regions where data SDs are deemed adequate while using more aggressive smoothing in areas where data appear noisy. Significance Statement The noisiness in the fields of polarimetric variables is an issue that has plagued dual-polarization weather radars since their inception. This is because standard deviations of polarimetric variable estimates increase significantly more with the reduction in SNR than those of spectral moment estimates. A typical mitigation approach that indiscriminately averages a fixed number of estimates in the range may lead to unnecessary loss of range resolution in regions where data appearance is satisfactory. Further, such an approach can be inadequate in regions with a high variability of estimates leading to insufficient enhancement of weather feature visibility. In this study, a method that mitigates these issues is proposed.

Research progress in the regulation of autophagy and mitochondrial homeostasis by AMPK signaling channels

AMP-activated protein kinase (AMPK) is a widely distributed and evolutionarily conserved serine/threonine protein kinase present in eukaryotic cells. In regulating cellular energy metabolism, AMPK plays an extremely important role as an energy metabolic kinase. When the body is in a low energy state, AMPK is activated in response to changes in intracellular adenine nucleotide levels and is bound to adenosine monophosphate (AMP) or adenosine diphosphate (ADP). Activated AMPK regulates various metabolic processes, including lipid and glucose metabolism and cellular autophagy. AMPK directly promotes autophagy by phosphorylating autophagy-related proteins in the mammalian target of rapamycin complex 1 (mTORC1), serine/threonine protein kinase-dysregulated 51-like kinase 1 (ULK1) and type III phosphatidylinositol 3-kinase-vacuolar protein-sorting 34 (PIK3C3-VPS34) complexes. AMPK also indirectly promotes autophagy by regulating the expression of downstream autophagy-related genes of transcription factors such as forkhead box O3 (FOXO3), lysosomal function transcription factor EB (TFEB) and bromodomain protein 4 (BRD4). AMPK also regulates mitochondrial autophagy, induces the division of damaged mitochondria and promotes the transfer of the autophagic response to damaged mitochondria. Another function of AMPK is to regulate mitochondrial health by stimulating mitochondrial biogenesis and participating in various aspects of mitochondrial homeostasis regulation. This review discusses the specific regulation of mitochondrial biology and internal environmental homeostasis by AMPK signaling channels as central to the cellular response to energy stress and regulation of mitochondria, highlighting the key role of AMPK in regulating cellular autophagy and mitochondrial autophagy, as well as advances in research on the regulation of mitochondrial homeostasis.

Bridging the Mind and Gut: Uncovering the Intricacies of Neurotransmitters, Neuropeptides, and their Influence on Neuropsychiatric Disorders.

The gut-brain axis (GBA) is a bidirectional signaling channel that facilitates communication between the gastrointestinal tract and the brain. Recent research on the gut-brain axis demonstrates that this connection enables the brain to influence gut function, which in turn influences the brain and its cognitive functioning. It is well established that malfunctioning of this axis adversely affects both systems' ability to operate effectively. Dysfunctions in the GBA have been associated with disorders of gut motility and permeability, intestinal inflammation, indigestion, constipation, diarrhea, IBS, and IBD, as well as neuropsychiatric and neurodegenerative disorders like depression, anxiety, schizophrenia, autism, Alzheimer's, and Parkinson's disease. Multiple research initiatives have shown that the gut microbiota, in particular, plays a crucial role in the GBA by participating in the regulation of a number of key neurochemicals that are known to have significant effects on the mental and physical well-being of an individual. Several studies have investigated the relationship between neuropsychiatric disorders and imbalances or disturbances in the metabolism of neurochemicals, often leading to concomitant gastrointestinal issues and modifications in gut flora composition. The interaction between neurological diseases and gut microbiota has been a focal point within this research. The novel therapeutic interventions in neuropsychiatric conditions involving interventions such as probiotics, prebiotics, and dietary modifications are outlined in this review. The findings of multiple studies carried out on mice show that modulating and monitoring gut microbiota can help treat symptoms of such diseases, which raises the possibility of the use of probiotics, prebiotics, and even dietary changes as part of a new treatment strategy for neuropsychiatric disorders and their symptoms. The bidirectional communication between the gut and the brain through the gut-brain axis has revealed profound implications for both gastrointestinal and neurological health. Malfunctions in this axis have been connected to a range of disorders affecting gut function as well as cognitive and neuropsychiatric well-being. The emerging understanding of the role of gut microbiota in regulating key neurochemicals opens up possibilities for novel treatment approaches for conditions like depression, anxiety, and neurodegenerative diseases.

Development of GNSS measurement models with the use of base stations and radio modules

Global navigation satellite systems (GNSS) historically been known as one of the newest technologies since the 1970s. GNSS originally developed for military purposes in the USA (GPS – Global Position System). There are several satellite systems in the world. Satelites, International Research Base Stations, regional/national and local base stations form a permanent geodetic frame. Research on the size and shape of the Earth-planet, climate, sea, urban planning. In geodesy, a network of global positioning base stations makes it possible to asses the movements of continents, land plates at international level. GNSS is an important technology in navigation, logistics, economics, land surveying and other “geo” sectors. GNSS equipment/receivers and their manufacturers are applying new designs and electronics. Initially GNSS instruments used with single frequency signal reception, later expanding the number of GNSS signal channels to two frequencies. Such technological improvements nowadays improve the certainty, reliability and accuracy – the overall quality – of GNSS measurements. The GNSS base station enables the surveyors, other user’s GNSS receiver to determine coordinates with an accuracy of two centimeters in real time (RTK) and with an accuracy of five millimetres using the accumulated post-processing data. Various types of factors hamper GNSS measurements. The GNSS signal (radio wave) travels in airspace, in urban environments and is a physical parameter. Any obstacle – tree, building walls, and atmospheric effect – makes GNSS measurements less accurate. The GNSS signal must be strong and free from attenuation and suppression effects. This study develops GNSS models that show the comparison, certainty and reliability of GNSS measurements using different types of GNSS techniques. Evaluation of Latvian Global Positioning Reference Station Network – LatPos system measurements against a corresponding RTK solution method using Latvian Global Positioning Network geodetic point (G2 class).

The value of environmental information disclosure: evidence from China’s carbon-reduction policy

ABSTRACT The impact of environmental information disclosure on firm value is unclear in the literature. Using China’s announcement of its carbon policy as an exogenous shock, this study examines the impact of environmental information disclosure. We find that stock prices increased faster for firms disclosing carbon information through the signalling channel. This study also lends support for disclosing carbon information.

Exploration of Four-Channel Coherent Optical Chaotic Secure Communication with the Rate of 400 Gb/s Using Photonic Reservoir Computing Based on Quantum Dot Spin-VCSELs

In this work, we present a novel four-channel coherent optical chaotic secure communication (COCSC) system, incorporating four simultaneous photonic reservoir computers in tandem with four coherent demodulation units. We employ a quartet of photonic reservoirs that capture the chaotic dynamics of four polarization components (PCs) emitted by a driving QD spin-VCSEL. These reservoirs are realized utilizing four PCs of a corresponding reservoir QD spin-VCSEL. Through these four concurrent photonic reservoir structures, we facilitate high-quality wideband-chaos synchronization across four pairs of PCs. Leveraging wideband chaos synchronization, our COCSC system boasts a substantial 4 × 100 GHz capacity. High-quality synchronization is pivotal for the precise demasking or decoding of four distinct signal types, QPSK, 4QAM, 8QAM and 16QAM, which are concealed within disparate chaotic PCs. After initial demodulation via correlation techniques and subsequent refinement through a variety of digital signal processing methods, we successfully reconstruct four unique baseband signals that conform to the QPSK, 4QAM, 8QAM and 16QAM specifications. Careful examination of the eye diagrams, bit error rates, and temporal trajectories of the coherently demodulated baseband signals indicates that each set of baseband signals is flawlessly retrieved. This is underscored by the pronounced eye openings in the eye diagrams and a negligible bit error rate for each channel of baseband signals. Our results suggest that delay-based optical reservoir computing employing a QD spin-VCSEL is a potent approach for achieving multi-channel coherent optical secure communication with optimal performance and enhanced security.

Satellite glial GPR37L1 and its ligand maresin 1 regulate potassium channel signaling and pain homeostasis.

G protein-coupled receptor 37-like 1 (GPR37L1) is an orphan GPCR with largely unknown functions. Here, we report that Gpr37l1/GRP37L1 ranks among the most highly expressed GPCR transcripts in mouse and human dorsal root ganglia (DRGs) and is selectively expressed in satellite glial cells (SGCs). Peripheral neuropathy induced by streptozotoxin (STZ) and paclitaxel (PTX) led to reduced GPR37L1 expression on the plasma membrane in mouse and human DRGs. Transgenic mice with Gpr37l1 deficiency exhibited impaired resolution of neuropathic pain symptoms following PTX- and STZ-induced pain, whereas overexpression of Gpr37l1 in mouse DRGs reversed pain. GPR37L1 is coexpressed with potassium channels, including KCNJ10 (Kir4.1) in mouse SGCs and both KCNJ3 (Kir3.1) and KCNJ10 in human SGCs. GPR37L1 regulates the surface expression and function of the potassium channels. Notably, the proresolving lipid mediator maresin 1 (MaR1) serves as a ligand of GPR37L1 and enhances KCNJ10- or KCNJ3-mediated potassium influx in SGCs through GPR37L1. Chemotherapy suppressed KCNJ10 expression and function in SGCs, which MaR1 rescued through GPR37L1. Finally, genetic analysis revealed that the GPR37L1-E296K variant increased chronic pain risk by destabilizing the protein and impairing the protein's function. Thus, GPR37L1 in SGCs offers a therapeutic target for the protection of neuropathy and chronic pain.

FC-TFS-CGRU: A Temporal-Frequency-Spatial Electroencephalography Emotion Recognition Model Based on Functional Connectivity and a Convolutional Gated Recurrent Unit Hybrid Architecture.

The gated recurrent unit (GRU) network can effectively capture temporal information for 1D signals, such as electroencephalography and event-related brain potential, and it has been widely used in the field of EEG emotion recognition. However, multi-domain features, including the spatial, frequency, and temporal features of EEG signals, contribute to emotion recognition, while GRUs show some limitations in capturing frequency-spatial features. Thus, we proposed a hybrid architecture of convolutional neural networks and GRUs (CGRU) to effectively capture the complementary temporal features and spatial-frequency features hidden in signal channels. In addition, to investigate the interactions among different brain regions during emotional information processing, we considered the functional connectivity relationship of the brain by introducing a phase-locking value to calculate the phase difference between the EEG channels to gain spatial information based on functional connectivity. Then, in the classification module, we incorporated attention constraints to address the issue of the uneven recognition contribution of EEG signal features. Finally, we conducted experiments on the DEAP and DREAMER databases. The results demonstrated that our model outperforms the other models with remarkable recognition accuracy of 99.51%, 99.60%, and 99.59% (58.67%, 65.74%, and 67.05%) on DEAP and 98.63%, 98.7%, and 98.71% (75.65%, 75.89%, and 71.71%) on DREAMER in a subject-dependent experiment (subject-independent experiment) for arousal, valence, and dominance.

Method and system for automated detection of sleep spindles using a single EEG channels based TEO and EMD

As a hallmark of N2 sleep stage, sleep spindle detection based on electroencephalogram (EEG) recordings plays a crucial role in analyzing sleep. Hence, how to effectively automatically detect sleep spindle is crucially important. However, many automatic detection methods assume that the signal is stationary, which is inconsistent with the observation that spindle signals have a significant change in amplitude. Thus, some important non-stationary information and the presented evident dynamic characteristics has been ignored.To overcome the aforementioned shortcoming, we propose a novel methodology based on Teager Energy Operator (TEO) and Empirical Mode Decomposition (EMD) to determine the exact location of sleep spindles using single-channel EEG signals. Because the TEO is sensitive to amplitude variations and energy features, our proposed method is suitable for capturing sleep spindles. We conduct plenty of experiments to evaluate our method. The experiments are conducted to validate the effectiveness of our method. On the DREAMS Sleep Spindles Database, our method achieves 91.83% ± 1.1%, 94.12% ± 3.46%, and 83.38% ± 9.32% for accuracy, specificity, and sensitivity, respectively. Experimental results show that the performance of the proposed methodology presented herein achieves better performance as compared to other methods. Moreover, owing to its usage of a single channel of EEG signal, the proposed method will be suitable for edge-device implementation to expedite sleep disorder diagnosis.

Probe-Type Multi-Core Fiber Optic Sensor for Simultaneous Measurement of Seawater Salinity, Pressure, and Temperature.

In this article, we propose and demonstrate a probe-type multi-core fiber (MCF) sensor for the multi-parameter measurement of seawater. The sensor comprises an MCF and two capillary optical fibers (COFs) with distinct inner diameters, in which a 45° symmetric core reflection (SCR) structure and a step-like inner diameter capillary (SIDC) structure filled with polydimethylsiloxane (PDMS) are fabricated at the fiber end. The sensor is equipped with three channels for different measurements. The surface plasmon resonance (SPR) channel (CHSPR) based on the side-polished MCF is utilized for salinity measurement. The fiber end air cavity, forming the Fabry-Pérot interference (FPI) channel (CHFPI), is utilized for pressure and temperature measurement. Additionally, the fiber Bragg grating (FBG) channel (CHFBG), which is inscribed in the central core, serves as temperature compensation for the measurement results. By combining three sensing principles with space division multiplexing (SDM) technology, the sensor overcomes the common challenges faced by multi-parameter sensors, such as channel crosstalk and signal demodulation difficulties. The experimental results indicate that the sensor has sensitivities of 0.36 nm/‱, -10.62 nm/MPa, and -0.19 nm/°C for salinity, pressure, and temperature, respectively. As a highly integrated and easily demodulated probe-type optical fiber sensor, it can serve as a valuable reference for the development of multi-parameter fiber optic sensors.

From apathy to activism: Shareholder dissent on director's pay proposals and board effectiveness

Using a hand-collected dataset of shareholder voting on 10,805 director compensation proposals at general shareholder meetings of Chinese listed firms from 2010 to 2019, we provide novel empirical evidence on board reactions to shareholder dissent. We find that boards of directors facing shareholder dissent significantly improve their monitoring and advising effectiveness. Specifically, they reduce the likelihood of financial restatements, improve CEO performance-turnover sensitivity, and improve corporate investment efficiency. Our findings are robust to propensity score matching, instrumental variable estimation and the exogenous shock of mandatory online voting. Moreover, independent directors facing dissent are more likely to vote against board proposals and to reduce their absences from board meetings. The mechanism analysis shows that shareholder dissent operates through the reputation concern and market signal channels. The effect of dissent is more prominent for non-SOEs, for firms with more high-profile directors, and for firms with less powerful CEOs. Besides, the improved board effectiveness induced by shareholder dissent leads to better firm performance. Overall, our findings highlight the role of shareholder voting in emerging markets with concentrated ownership and weak investor protection.

Differential Cantilever Enhanced Fiber-Optic Photoacoustic Sensor for Diffusion Gas Detection.

Aiming at the problem of the fiber-optic photoacoustic (PA) sensor being easily disturbed by external vibration and noise, a differential cantilever enhanced fiber-optic PA sensor is proposed for diffusion gas detection. The sensor comprises two PA tubes with the same structure and a pair of differential interferometric cantilevers. The two PA tubes are symmetrically distributed. The laser is incident on the PA tube as the signal channel to excite the PA pressure wave. Another tube without incident laser is used as the reference channel to suppress external disturbance. The external interference signals and PA signals superimposed with disturbance are detected by the differential cantilevers from the two channels. The signals are simultaneously restored by a single white-light interferometry demodulator, which multiplexed the spectral frequency domain of the superimposed interference spectrum. The experimental results show that the suppression effect of the differential cantilever enhanced PA sensor on ambient noise is improved by 80%, compared to the traditional single-cantilever sensor. The external cofrequency disturbance is suppressed by 20.9 dB. The minimum detection limit to acetylene (C2H2) downs to about 60 ppb with an integration time of 100 s. The sensor has excellent antivibration and electromagnetic interference ability.

Seizure prediction based on improved vision transformer model for EEG channel optimization

Epileptic seizures are unpredictable events caused by abnormal discharges of a patient’s brain cells. Extensive research has been conducted to develop seizure prediction algorithms based on long-term continuous electroencephalogram (EEG) signals. This paper describes a patient-specific seizure prediction method that can serve as a basis for the design of lightweight, wearable and effective seizure-prediction devices. We aim to achieve two objectives using this method. The first aim is to extract robust feature representations from multichannel EEG signals, and the second aim is to reduce the number of channels used for prediction by selecting an optimal set of channels from multichannel EEG signals while ensuring good prediction performance. We design a seizure-prediction algorithm based on a vision transformer (ViT) model. The algorithm selects channels that play a key role in seizure prediction from 22 channels of EEG signals. First, we perform a time-frequency analysis of processed time-series signals to obtain EEG spectrograms. We then segment the spectrograms of multiple channels into many non-overlapping patches of the same size, which are input into the channel selection layer of the proposed model, named Sel-JPM-ViT, enabling it to select channels. Application of the Sel-JPM-ViT model to the Boston Children’s Hospital–Massachusetts Institute of Technology scalp EEG dataset yields results using only three to six channels of EEG signals that are slightly better that the results obtained using 22 channels of EEG signals. Overall, the Sel-JPM-ViT model exhibits an average classification accuracy of 93.65%, an average sensitivity of 94.70% and an average specificity of 92.78%.

Efficient methods for iterative ultrasound image reconstruction using L1 and L2 norm regularization

Ultrasound image reconstruction can be posed as an inverse problem, with image pixels as parameters to a model of wave propagation that are estimated from raw ultrasound channel data. Such framework allows one to make assumptions about channel signals and image properties in form of regularization that can be used to improve reconstruction accuracy in the presence of electronic and acoustic noise. A simple model assumes spherical wave propagation from point sources in a homogeneous medium, similar to delay-and-sum (DAS) beamforming; solving such problems numerically can require many iterations and can result in blurred images when traditional L2 norm regularization is used. We use Fast Iterative Shrinkage Thersholding Algorithm (FISTA) to reduce the number of iterations to less than 10, and to apply L1-norm regularization to the data, which improves edge sharpness and image contrast. We demonstrate the concept using FIELD II simulated ultrasound signals from point, speckle, and anechoic targets with controlled levels of electronic and speckle noise. The FISTA-reconstructed point targets show reduced sidelobe levels by 15 dB compared to the traditional DAS image, and the reduction in full-width at half maximum by a factor of 2. We also implement iterative reconstruction in omega-k frequency domain, which allows factoring the forward and adjoint operators at each spatial frequency and paves the way for an intuitive and more memory efficient, multi-threaded implementation of the method.

Blind passive signal detection via dictionary learning in unknown multipath time-spreading distortion underwater channels

Blind passive signal detection is a challenging endeavor since typically there is no prior knowledge of the transmitted signal. This becomes further complicated in time-spreading distortion (TSD) underwater channels [R. Rashid, E. Zhang, A. Abdi, and Z. H. Michalopoulou, “Theoretical and experimental multi-sensor signal detection in time spreading distortion underwater channels,” Proc. Oceans (2022)], where due to the presence of multiple propagation paths, the unknown signal is convolved with an unknown channel impulse response. In this paper, we introduce and develop a blind passive signal detection method, using dictionary learning [M. Sadeghi, M. Babaie-Zadeh, and C. Jutten, “Dictionary learning for sparse representation: A novel approach,” IEEE Signal Process. Lett. (2013)]. In our blind passive signal detection method, we use the received data to estimate the unknown signal, and also to separate it from the unknown channel impulse response. We have conducted simulations and underwater experiments, to generate receiver operating characteristic curves, to study the performance of the proposed method. The high detection probabilities of the blind method, compared to the replica correlation integration method—that needs to know the transmitted signal for matched filtering—demonstrate the usefulness of the method for passive detection of unknown signals in unknown TSD channels.

Enhancing AmBC Systems With Deep Learning for Joint Channel Estimation and Signal Detection

Enhancing AmBC Systems With Deep Learning for Joint Channel Estimation and Signal Detection

Linear Anti-interference Algorithm for Digital Signal Transmission in Fiber Optic Communication Networks based on Link Analysis

In order to achieve accurate transmission of protection signals in fiber optic communication networks, it is necessary to perform channel balancing configuration of fiber optic communication networks and adaptive forwarding control processing of relay protection signals, the author proposes an accurate transmission method for relay protection signals in fiber optic communication networks based on time-varying multipath fading suppression and adaptive beamforming. The system analyzes the sources of wireless long-distance pain signal interference signals, introduces anti-interference technologies such as two-dimensional joint processing (STAP), provides anti-interference algorithms and related gain analysis, and conducts signal processing gain simulation using MATLAB. Based on the analysis of comprehensive simulation results, at a given symbol length, the signal bandwidth increases, and the processing gain infinitely approaches the given theoretical limit value, rather than increasing nonlinearly. The reason is that the channel is affected by noise, and the channel estimation value and signal conjugate multiplication produce a noise quadratic term. At this point, the estimated value of the coherent region channel is reduced by the influence of noise, and the signal-to-noise ratio loss caused by the noise quadratic term is reduced, so the processing gain increases. During the process of infinite increase in signal bandwidth, the input signal-to-noise power ratio of the receiver tends to decrease towards an infinite value, limited by the size of the coherent region. The channel estimation value increases under the influence of noise, and the noise quadratic term is the main factor affecting the output noise power. When the symbol length is greater than the coherent time, the smaller the maximum Doppler frequency shift and the larger the coherent detection area, the greater the processing gain.

Allyl isothiocyanate, a TRPA1 agonist, protects against olanzapine-induced hypothalamic and hepatic metabolic aberrations in female mice

Olanzapine, a widely prescribed atypical antipsychotic, poses a great risk to the patient’s health by fabricating a plethora of severe metabolic and cardiovascular adverse effects eventually reducing life expectancy and patient compliance. Its heterogenous receptor binding profile has made it difficult to point out a specific cause or treatment for the related side effects. Growing body of evidence suggest that transient receptor potential (TRP) channel subfamily Ankyrin 1 (TRPA1) has pivotal role in pathogenesis of type 2 diabetes and obesity. With this background, we aimed to investigate the role of pharmacological manipulations of TRPA1 channels in antipsychotic (olanzapine)-induced metabolic alterations in female mice using allyl isothiocyanate (AITC) and HC-030031 (TRPA1 agonist and antagonist, respectively). It was found that after 6 weeks of treatment, AITC prevented olanzapine-induced alterations in body weight and adiposity; serum, and liver inflammatory markers; glucose and lipid metabolism; and hypothalamic appetite regulation, nutrient sensing, inflammatory and TRPA1 channel signaling regulating genes. Furthermore, several of these effects were absent in the presence of HC-030031 (TRPA1 antagonist) indicating protective role of TRPA1 agonism in attenuating olanzapine-induced metabolic alterations. Supplementary in-depth studies are required to study TRPA1 channel effect on other aspects of olanzapine-induced metabolic alterations.

Single channel blind source separation of rolling bearing compound faults based on self-learning sparse decomposition and feature mode decomposition

Feature mode decomposition (FMD) has advantages over the other newer time-frequency methods such as ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) in single channel blind source separation (SCBSS). However, FMD has the defect of needing to determine the precise number of fault sources manually. To solve the above defect of FMD, an adaptive method for determining the number of fault sources based on the shift invariant sparse code (SISC) is proposed. SISC was used to train a set of basis functions from the single channel signal, and the corresponding potential components were reconstructed firstly. Subsequently, the structural similarity of these potential components was used for clustering, and each of the obtained clustering signals represented one kind of fault. Then the number of clustering was determined by minimizing the structural correlation among the clustering signals. It was considered that the source separation had achieved the best effect when the structural difference among the clusters was the largest, and the number of clustering at this time was used as the optimal estimated value, which was used as the modal inputs number of FMD calculation model to realize SCBSS of rolling bearing. Simulation and experimental analysis were carried out to verify the effectiveness of the proposed method, and its superiority was also verified through comparison.