Signaling Components Research Articles

An Ultra-Short-Term Wind Power Prediction Method Based on Quadratic Decomposition and Multi-Objective Optimization

To augment the accuracy, stability, and qualification rate of wind power prediction, thereby fostering the secure and economical operation of wind farms, a method predicated on quadratic decomposition and multi-objective optimization for ultra-short-term wind power prediction is proposed. Initially, the original wind power signal is decomposed using a quadratic decomposition method constituted by the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Fuzzy Entropy (FE), and Symplectic Geometry Mode Decomposition (SGMD), thereby mitigating the randomness and volatility of the original signal. Subsequently, the decomposed signal components are introduced into the Deep Bidirectional Long Short-Term Memory (DBiLSTM) neural network for time series modeling, and the Sand Cat Swarm Optimization Algorithm (SCSO) is employed to optimize the network hyperparameters, thereby enhancing the network’s predictive performance. Ultimately, a multi-objective optimization loss that accommodates accuracy, stability, and grid compliance is proposed to guide network training. Experimental results reveal that the employed quadratic decomposition method and the proposed multi-objective optimization loss can effectively bolster the model’s predictive performance. Compared to other classical methods, the proposed method achieves optimal results across different seasons, thereby demonstrating robust practicality.

A novel sequence component based fault detection index for microgrid protection

The paper introduces a novel protective relaying method using symmetrical components of current to detect asymmetrical faults in microgrid. This approach is focused on utilizing the complex magnitude-phase plane (MPP) of the fault detection index (FDI), which is computed using the positive and negative sequence components of the current signal retrieved at one end of the faulty line. The MPP obtained from the settling values of the FDI's magnitude (MFDI) and FDI's angle (AFDI) after the fault inception, is considered as the key indicator for fault detection. The scheme is comprehensively evaluated for a wide range of fault situations including variations in fault types, fault locations, fault resistances and DG penetrations in grid-connected (GC) and non-grid-connected (NGC) modes of operation. Several critical no fault cases are also tested to show the efficacy of the proposed logic for false operation. The fault study is conducted extensively on a modified low voltage standard Consortium for Electric Reliability Technology Solutions (CERTS) microgrid and a standard IEEE-34 bus distribution system including DGs. Furthermore, the proposed FDI-based scheme is tested on MATLAB/Simulink platform and validated on Typhoon HIL platform to assess the real-time performance. The test results show that this FDI-based scheme using the MPP concept can be a potent solution for the protection of microgrid.

Autophagy receptor ZmNBR1 promotes the autophagic degradation of ZmBRI1a and enhances drought tolerance in maize.

Drought stress is a crucial environmental factor that limits plant growth, development, and productivity. Autophagy of misfolded proteins can help alleviate the damage caused in plants experiencing drought. However, the mechanism of autophagy-mediated drought tolerance in plants remains largely unknown. Here, we cloned the gene for a maize (Zea mays) selective autophagy receptor, NEXT TO BRCA1 GENE 1 (ZmNBR1), and identified its role in the response to drought stress. We observed that drought stress increased the accumulation of autophagosomes. RNA sequencing and reverse transcription-quantitative polymerase chain reaction showed that ZmNBR1 is markedly induced by drought stress. ZmNBR1 overexpression enhanced drought tolerance, while its knockdown reduced drought tolerance in maize. Our results established that ZmNBR1 mediates the increase in autophagosomes and autophagic activity under drought stress. ZmNBR1 also affects the expression of genes related to autophagy under drought stress. Moreover, we determined that BRASSINOSTEROID INSENSITIVE 1A (ZmBRI1a), a brassinosteroid receptor of the BRI1-like family, interacts with ZmNBR1. Phenotype analysis showed that ZmBRI1a negatively regulates drought tolerance in maize, and genetic analysis indicated that ZmNBR1 acts upstream of ZmBRI1a in regulating drought tolerance. Furthermore, ZmNBR1 facilitates the autophagic degradation of ZmBRI1a under drought stress. Taken together, our results reveal that ZmNBR1 regulates the expression of autophagy-related genes, thereby increasing autophagic activity and promoting the autophagic degradation of ZmBRI1a under drought stress, thus enhancing drought tolerance in maize. These findings provide new insights into the autophagy degradation of brassinosteroid signaling components by the autophagy receptor NBR1 under drought stress.

Recruitment of HAB1 and SnRK2.2 by C2-domain protein CAR1 in plasma membrane ABA signaling.

Plasma membrane (PM)-associated abscisic acid (ABA) signal transduction is an important component of ABA signaling. The C2-domain ABA-related (CAR) proteins have been reported to play a crucial role in recruiting ABA receptor PYR1/PYL/RCAR (PYLs) to the PM. However, the molecular details of the involvement of CAR proteins in membrane-delimited ABA signal transduction remain unclear. For instance, where this response process takes place and whether any additional members besides PYL are taking part in this signaling process. Here, the GUS-tagged materials for all Arabidopsis CAR members were used to comprehensively visualize the extensive expression patterns of the CAR family genes. Based on the representativeness of CAR1 in response to ABA, we determined to use it as a target to study the function of CAR proteins in PM-associated ABA signaling. Single-particle tracking showed that ABA affected the spatiotemporal dynamics of CAR1. The presence of ABA prolonged the dwell time of CAR1 on the membrane and showed faster lateral mobility. Surprisingly, we verified that CAR1 could directly recruit hypersensitive to ABA1 (HAB1) and SNF1-related protein kinase 2.2 (SnRK2.2) to the PM at both the bulk and single-molecule levels. Furthermore, PM localization of CAR1 was demonstrated to be related to membrane microdomains. Collectively, our study revealed that CARs recruited the three main components of ABA signaling to the PM to respond positively to ABA. This study deepens our understanding of ABA signal transduction.

BoaBZR1.1 mediates brassinosteroid-induced carotenoid biosynthesis in Chinese kale

Abstract Brassinazole resistant 1 (BZR1), a brassinosteroid (BR) signaling component, plays a pivotal role in regulating numerous specific developmental processes. Our study demonstrated that exogenous treatment with 2,4-epibrassinolide (EBR) significantly enhanced the accumulation of carotenoids and chlorophylls in Chinese kale (Brassica oleracea var. alboglabra). The underlying mechanisms was deciphered through yeast one-hybrid (Y1H) and dual-luciferase (LUC) assays, whereby BoaBZR1.1 directly interacts with the promoters of BoaCRTISO and BoaPSY2, activating their expression. This effect was further validated through overexpression of BoaBZR1.1 in Chinese kale calli and plants, both of which exhibited increased carotenoid accumulation. Additionally, qPCR analysis unveiled upregulation of carotenoid and chlorophyll biosynthetic genes in the T1 generation of BoaBZR1.1-overexpressing plants. These findings underscored the significance of BoaBZR1.1-mediated BR signaling in regulating carotenoid accumulation in Chinese kale and suggested the potential for enhancing the nutritional quality of Chinese kale through genetic engineering of BoaBZR1.1.

Blocked versus interleaved: How range contexts modulate time perception and its EEG signatures.

Accurate time perception is a crucial element in a wide range of cognitive tasks, including decision-making, memory, and motor control. One commonly observed phenomenon is that when given a range of time intervals to consider, people's estimates often cluster around the midpoint of those intervals. Previous studies have suggested that the range of these intervals can also influence our judgments, but the neural mechanisms behind this "range effect" are not yet understood. We used both behavioral tests and electroencephalographic (EEG) measures to understand how the range of sample time intervals affects the accuracy of people's subsequent time estimates. Study participants were exposed to two different setups: In the "blocked-range" (BR) session, short and long intervals were presented in separate blocks, whereas in the "interleaved-range" (IR) session, intervals of various lengths were presented randomly. Our findings indicated that the BR context led to more accurate time estimates compared to the IR context. In terms of EEG data, the BR context resulted in quicker buildup of contingent negative variation (CNV), which also reached higher amplitude levels and dissolved more rapidly during the encoding stage. We also observed an enhanced amplitude in the offset P2 component of the EEG signal. Overall, our results suggest that the variability in time intervals, as defined by their range, influences the neural processes that underlie time estimation.

Transmission tower bolt-loosening time–frequency analysis and localization method considering time-varying characteristics

To address the issues of high concealment and difficult positioning of loose bolts in transmission towers, this paper proposes a new method for locating loose bolts in transmission towers. In this method, we divide the vibration response of the transmission tower into low-frequency signals of 2–25 Hz and high-frequency signals of 25–75 Hz. For the low-frequency signals, the single signal component is obtained by adaptive Chirp mode decomposition and uses the general demodulation transformation to deeply denoise the non-modal information. Since frequency characteristics themselves do not contain time information, considering the importance of time information for positioning, we propose a low-frequency time-varying frequency feature that preserves time characteristics based on synchronous wavelet transform and peak search. For the high-frequency signals, we use singular value decomposition to remove signal outliers caused by pulse excitation and eliminate forced vibrations through wavelet packet transform. Without altering its inherent characteristics, this method enables high-frequency time-domain signals to better represent the nonlinear characteristics of transmission towers. Furthermore, based on the powerful capabilities of Timesnet and Transformer in dealing with time series data, we propose a fault diagnosis model, which ultimately achieves the positioning of loose bolts in transmission towers. The bolt node model proves that this approach can better represent the loose bolt characteristics, and the transmission tower model verifies the effectiveness of this approach in locating loose bolts in transmission towers. Finally, bolt-loosening tests were conducted on a 110 kV transmission tower, and the accuracy of the positioning results reached 92.8%, demonstrating the effectiveness and efficiency of this method in practical positioning applications.

Denoising Seismic Waveforms Using a Wavelet-Transform-Based Machine-Learning Method

ABSTRACT Seismic waveform data recorded at stations can be thought of as a superposition of the signal from a source of interest and noise from other sources. Frequency-based filtering methods for waveform denoising do not result in desired outcomes when the targeted signal and noise occupy similar frequency bands. Recently, denoising techniques based on deep-learning convolutional neural networks (CNNs), in which a recorded waveform is decomposed into signal and noise components, have led to improved results. These CNN methods, which use short-time Fourier transform representations of the time series, provide signal and noise masks for the input waveform. These masks are used to create denoised signal and designaled noise waveforms, respectively. However, advancements in the field of image denoising have shown the benefits of incorporating discrete wavelet transforms (DWTs) into CNN architectures to create multilevel wavelet CNN (MWCNN) models. The MWCNN model preserves the details of the input due to the good time–frequency localization of the DWT. Here, we use a data set of over 382,000 constructed seismograms recorded by the University of Utah Seismograph Stations network to compare the performance of CNN and MWCNN-based denoising models. Evaluation of both models on constructed test data shows that the MWCNN model outperforms the CNN model in the ability to recover the ground-truth signal component in terms of both waveform similarity and preservation of amplitude information. Model evaluation of real-world data shows that both the CNN and MWCNN models outperform standard band-pass filtering (BPF; average improvement in signal-to-noise ratio of 9.6 and 19.7 dB, respectively, with respect to BPF). Evaluation of continuous data suggests the MWCNN denoiser can improve both signal detection capabilities and phase arrival time estimates.

Shaping brassinosteroid signaling through scaffold proteins.

Cellular responses to internal and external stimuli are orchestrated by intricate intracellular signaling pathways. To ensure an efficient and specific information flow, cells employ scaffold proteins as critical signaling organizers. With the ability to bind multiple signaling molecules, scaffold proteins can sequester signaling components within specific subcellular domains or modulate the efficiency of signal transduction. Scaffolds can also tune the output of signaling pathways by serving as regulatory targets. This review focuses on scaffold proteins associated with the plant GLYCOGEN SYNTHASE KINASE3-like kinase, BRASSINOSTEROID-INSENSITIVE2 (BIN2) that serve as a key negative regulator of brassinosteroid (BR) signaling. Here we summarize the current understanding of how scaffold proteins actively shape BR signaling outputs and crosstalk in plant cells via interactions with BIN2.

Abstract LB450: Mechanisms of acquired resistance to AKT inhibitor capivasertib in AKT1 mutant patient derived breast cancer models

Abstract Capivasertib, a pan-AKT inhibitor, has recently been approved in combination with fulvestrant to treat ER+ HER2- breast cancer patients with one or more of PI3KCA/AKT1/PTEN alterations. However, as with many targeted therapies acquired drug resistance remains a challenge. In this study, we investigated the mechanisms of acquired capivasertib resistance in AKT1 mutant breast cancer models. We established two estrogen receptor positive (ER+) AKT1 E17K mutant breast cancer patient derived organoids (PDOs), that were sensitive to capivasertib in vitro. PDOs were chronically exposed to 1µM capivasertib. PDOs were sequenced (DNA & RNA) at multiple timepoints of treatment (3 days, 4 and 9 weeks after development of resistance). Using CRISPR-Cas9 editing we also engineered CAMA1 (ER+) cells to express AKT1 E17K, and generated derived resistant AKT1 mutant and parental models in response to continuous capivasertib exposure. CAMA1 AKT1 mutant and parental resistant were screened with an siRNA Kinome library supplemented with genes from the PDO RNA-sequencing. Phosphoproteomics was performed on capivasertib resistant E17K models in the presence and absence of capivasertib. PDO models persisting after either 4 or 9 weeks of capivasertib treatment showed no acquisition of genetic driver events, or changes in AKT copy number. Resistant organoids at 9 weeks treatment showed increased expression of ERBB3 (HER3), EGFR3 and LPAR5 as well as increased expression of MAPK signalling components. In siRNA screens on CAMA1 derived resistant models, siRNA targeting ERBB2, CDK4, PLK4 and CHEK1, ESR1 and LPAR5 modulated sensitivity to capivasertib. Analysis of phosphoproteins in capivasertib resistant models revealed reactivation of mTORC1 signalling despite sustained AKT inhibition, with dramatic upregulation of PDK1 and mTORC2. Consistent with the siRNA screens, drug combinations identified fulvestrant as a synergistic agent with capivasertib in the resistant E17K models. Sensitivity to mTOR inhibition was maintained in capivasertib resistant models, with mTOR inhibition re-sensitizing cells to capivasertib. In these models, acquired resistance to capivasertib occurs through upregulation of ER signalling and rewiring of pathway signalling to bypass AKT inhibition and reactivate mTORC1. Targeting these mechanisms could prolong the use of capivasertib, with future work focusing on validating these mechanisms and re-sensitizing drug combinations in other AKT1 mutant models & PDOs. Citation Format: Sarah Mearns, Alex Pearson, Li-Xuan Sim, Rosalind Cutts, Prithika Sritharan, Heena Shah, Aditi Gulati, Stuart Williamson, Simon Barry, Elza De-Bruin, Nicholas C. Turner. Mechanisms of acquired resistance to AKT inhibitor capivasertib in AKT1 mutant patient derived breast cancer models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB450.

Dynamic Spectrum Sensing For 5G Cognitive Radio Networks Using Optimization Technique

With increasing development of 5G technology, the rapid growth of various technologies and the growth of various wireless devices, demand for wireless spectrum becomes more urgent. Wireless communication technologies have been advancing rapidly, leading to the emergence of 5G communication systems .Spectrum sensing is the key model utilized to access the spectrum dynamically in CRN. Various researchers are done in spectrum sensing scenario and different methods are designed to perform the task of spectrum resource sharing. Most of the methods design a decision statistics for identifying the signal by analyzing the features of noise and signals. One promising approach to address the challenges of spectrum sensing in cognitive radio networks for 5G communication is the integration of deep learning with hybrid optimization techniques.  By combining the power of deep learning algorithms with optimization methods, it becomes possible to improve the efficiency and accuracy of spectrum sensing in dynamic and diverse communication scenarios. One of the key advantages of MIMO-based spectrum sensing is its ability to exploit the spatial diversity inherent in the environment. By using multiple antennas at both the transmitter and receiver, MIMO systems can distinguish between signals arriving from different directions, thereby improving the accuracy of spectrum sensing. Moreover, MIMO technology also enables the cognitive radio network to utilize the available spectrum more efficiently. With the ability to establish multiple parallel communication links, MIMO-based cognitive radio networks can achieve higher data rates and improved spectral efficiency, especially in dynamic and challenging radio environments. Research in this field is focused on further enhancing the performance of MIMO-based spectrum sensing in cognitive radio networks through advanced signal processing algorithms and machine learning technique  Spectrum sensing detect existence of primary users (PUs) and it becomes a main research topic of CRN in industry and academic domain. This research developed a new framework based on algorithm to progress the mechanism of spectrum sensing in the CRN by detecting the availability of free channel. The signal components are extracted from the received signal and thereby spectrum availability of detected through fusion center using proposed Feedback Artificial Optimization Algorithm-based Deep Q network. However, Simulation results show that the proposed  multiple-input-multiple-output(MIMO) spectrum sensing method achieves good performance Cognitive Radion Network attains maximum probability of detection and minimum probability of false alarm as 70%, and 38% for Rayleigh channel.

Interrupted sampling repeater jamming suppression based on multiple extended complex‐valued convolutional auto‐encoders

AbstractInterrupted sampling repeater jamming (ISRJ) with flexible modulation parameters and coherent processing gain seriously threatens the radar detection system. The jamming suppression and target detection performance of existing anti‐jamming methods are limited by strong noise and jamming signals. An ISRJ suppression method combining multiple extended complex‐valued convolutional auto‐encoders (CVCAEs) and compressed sensing (CS) reconstruction is proposed. For the different tasks of parameter estimation and signal denoising, the extended CVCAEs including a complex‐valued convolutional shrinkage network (CVCSNet) and a complex‐valued UNet (CVUNet) are developed. Based on the time‐domain discontinuity of ISRJ signals, the CVCSNet is first used to directly estimate the parameters representing signal components and extract jamming‐free signals from received signals. Then, the extracted signals are denoised using the CVUNet. After that, relying on the denoised signals and the frequency sparsity of de‐chirped target signals, a CS model is established and solved to recover complete target signals for jamming suppression. Utilising the advantages of deep neural networks in weak feature extraction and signal representation, the CVCSNet and CVUNet can effectively improve the signal extraction accuracy and alleviate the limitation of noise on target signal reconstruction. Experimental results verify that the proposed method has superior ISRJ suppression performance and is robust to varying signal‐to‐noise ratios, jamming‐to‐signal ratios and jamming parameters.

Vehicle Response-Based Bridge Modal Identification Using Different Time–Frequency Analysis Methods

Modal identification of bridges based on the responses of moving vehicles, termed as the indirect method, has drawn much attention in recent years. This indirect method has been proven to be capable of identifying bridge frequencies and mode shapes in the feasibility research. However, in the presence of additional factors such as vehicle speed and road surface roughness, it is difficult to identify high-order frequencies and high-resolution mode shapes of the bridge. The authors previously proposed a tractor-double-trailers model and the time–domain subtraction method to obtain the relative displacement of two trailers for bridge modal identification. In this study, the authors develop a methodology to improve the identification performance by combining the EMD technique and the time–frequency analysis method. The EMD technique is applied to separate the multi-source response signals of the trailers to obtain the signal component that contains only the bridge vibration. Then, different time–frequency analysis methods such as the short-time Fourier transform (STFT), Wigner–Ville distribution (WVD), and continuous wavelet transform (CWT) are adopted to construct the high-resolution bridge mode shapes. Numerical studies with one-dimensional and three-dimensional bridge models are conducted to verify the proposed method and investigate the effects of different factors. Finally, a laboratory test is implemented on a scaled bridge to verify the performance of the proposed method. The results show that the proposed method is able to separate the vehicle frequency and bridge frequencies, which facilitates the identification of bridge frequencies from the dynamic response of moving vehicles. Meanwhile, the CWT method performs the best in the identification of modal shapes among the three time–frequency analysis methods.

A study to investigate the anticancer potential of carvacrol via targeting Notch signaling in breast cancer

Abstract Breast cancer (BC) continues to be a primary worldwide health concern despite the tremendous efforts made to deploy novel chemotherapeutic techniques for the treatment of BC. It is, therefore, essential to elucidate better plant-based compounds targeting deregulated signaling components in various cancer cell types. Our objective was to elucidate a potent targeted therapeutic approach by exploiting the anticancerous potential of carvacrol in MDA-MB-231 cells via employing silicon and in vitro approaches. In silico analysis was executed to identify the anticancer potential of carvacrol against BC via targeting crucial signaling component of the NOTCH pathway, namely Jagged-1 and its downstream target cyclin D1. In vitro, assays were also employed to display the antiproliferative potential of carvacrol at the mRNA level in MDA-MB-231 cells via targeting Jagged-1 and cyclin D1 genes. Docking studies using CB DOCK displayed better binding energy of carvacrol (Jagged-1: −5.0 and cyclin D1: −5.8) in comparison to the standard drug, 5-fluorouracil (Jagged-1: −4.5; cyclin D1: −4.6) against these crucial targets. Carvacrol potentially downregulated the expression of these crucial genes along with caspase-mediated apoptosis induction. However, more in vitro assays must be employed to validate its candidature for drug development against BC. This study provided a novel insight into the targeted therapeutic approach using natural products and deregulated signaling components for managing breast carcinoma.

A DENOISING METHOD FOR COLONIC PRESSURE SIGNAL BASED ON CEEMDAN-WT

Colonic pressure is a principal parameter reflecting intestinal motility, and is also an important basis for diagnosing intestinal motility disorders. The physiological signals of the colon are mixed with the disturbing signals such as breathing and heartbeat due to the complex environment of the intestine. It is essential to find an effective method to reduce the noise in the signals. To solve this problem, a combined denoising method of colonic pressure signals is proposed in this paper, which combines the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet transform. In this method, colonic peristalsis pressure signal is first separated out by wavelet transform, then decomposed by CEEMDAN. According to pearson correlation coefficient, the high-frequency intrinsic mode functions (IMFs) with noise was selected and denoised by wavelet threshold. Finally, the signal was reconstructed by combining the reserved IMFs with the denoised IMFs. The validity of the proposed method is verified by the real colon pressure data collected by the wireless motility capsule in this paper. The experimental results show that the combined denoising method can deal with the noise better than the single denoising method on the premise of fully retaining the real components of the colonic pressure signal.

Sulfate Availability and Hormonal Signaling in the Coordination of Plant Growth and Development.

Sulfur (S), one of the crucial macronutrients, plays a pivotal role in fundamental plant processes and the regulation of diverse metabolic pathways. Additionally, it has a major function in plant protection against adverse conditions by enhancing tolerance, often interacting with other molecules to counteract stresses. Despite its significance, a thorough comprehension of how plants regulate S nutrition and particularly the involvement of phytohormones in this process remains elusive. Phytohormone signaling pathways crosstalk to modulate growth and developmental programs in a multifactorial manner. Additionally, S availability regulates the growth and development of plants through molecular mechanisms intertwined with phytohormone signaling pathways. Conversely, many phytohormones influence or alter S metabolism within interconnected pathways. S metabolism is closely associated with phytohormones such as abscisic acid (ABA), auxin (AUX), brassinosteroids (BR), cytokinins (CK), ethylene (ET), gibberellic acid (GA), jasmonic acid (JA), salicylic acid (SA), and strigolactones (SL). This review provides a summary of the research concerning the impact of phytohormones on S metabolism and, conversely, how S availability affects hormonal signaling. Although numerous molecular details are yet to be fully understood, several core signaling components have been identified at the crossroads of S and major phytohormonal pathways.

Regulation of adaptive growth decisions via phosphorylation of the TRAPPII complex in Arabidopsis.

Plants often adapt to adverse or stress conditions via differential growth. The trans-Golgi network (TGN) has been implicated in stress responses, but it is not clear in what capacity it mediates adaptive growth decisions. In this study, we assess the role of the TGN in stress responses by exploring the previously identified interactome of the Transport Protein Particle II (TRAPPII) complex required for TGN structure and function. We identified physical and genetic interactions between AtTRAPPII and shaggy-like kinases (GSK3/AtSKs) and provided in vitro and in vivo evidence that the TRAPPII phosphostatus mediates adaptive responses to abiotic cues. AtSKs are multifunctional kinases that integrate a broad range of signals. Similarly, the AtTRAPPII interactome is vast and considerably enriched in signaling components. An AtSK-TRAPPII interaction would integrate all levels of cellular organization and instruct the TGN, a central and highly discriminate cellular hub, as to how to mobilize and allocate resources to optimize growth and survival under limiting or adverse conditions.

Early Fault Diagnosis of Bearings Based on Symplectic Geometry Mode Decomposition Guided by Optimal Weight Spectrum Index

When the rotating machinery fails, the signal generated by the faulty component often no longer maintains the original symmetry, which makes the vibration signal with nonlinear and non-stationary characteristics, and is easily affected by background noise and other equipment excitation sources. In the early stage of fault occurrence, the fault signal is weak and difficult to extract. Traditional fault diagnosis methods are not able to easily diagnose fault information. To address this issue, this paper proposes an early fault diagnosis method for symplectic geometry mode decomposition (SGMD) based on the optimal weight spectrum index (OWSI). Firstly, using normal and fault signals, the optimal weight spectrum is derived through convex optimization. Secondly, SGMD is used to decompose the fault signal, obtaining a series of symplectic geometric modal components (SGCs) and calculating the optimal weight index of each component signal. Finally, using the principle of maximizing the OWSI, sensitive components reflecting fault characteristics are selected, and the signal is reconstructed based on this index. Then, envelope analysis is performed on the sensitive components to extract early fault characteristics of rolling bearings. OWSI can effectively distinguish the interference components in fault signals using normal signals, while SGMD has the characteristic of unchanged phase space structure, which can effectively ensure the integrity of internal features in data. Using actual fault data of rolling bearings for verification, the results show that the proposed method can effectively extract sensitive components that reflect fault characteristics. Compared with existing methods such as Variational Mode Decomposition (VMD), Feature Mode Decomposition (FMD), and Spectral Kurtosis (SK), this method has better performance.

Ultraviolet vision in anemonefish improves colour discrimination.

In many animals, ultraviolet (UV) vision guides navigation, foraging, and communication, but few studies have addressed the contribution of UV signals to colour vision, or measured UV discrimination thresholds using behavioural experiments. Here, we tested UV colour vision in an anemonefish (Amphiprion ocellaris) using a five-channel (RGB-V-UV) LED display. We first determined that the maximal sensitivity of the A. ocellaris UV cone was ∼386 nm using microspectrophotometry. Three additional cone spectral sensitivities had maxima at ∼497, 515 and ∼535 nm. We then behaviourally measured colour discrimination thresholds by training anemonefish to distinguish a coloured target pixel from grey distractor pixels of varying intensity. Thresholds were calculated for nine sets of colours with and without UV signals. Using a tetrachromatic vision model, we found that anemonefish were better (i.e. discrimination thresholds were lower) at discriminating colours when target pixels had higher UV chromatic contrast. These colours caused a greater stimulation of the UV cone relative to other cone types. These findings imply that a UV component of colour signals and cues improves their detectability, which likely increases the prominence of anemonefish body patterns for communication and the silhouette of zooplankton prey.

Polarization characteristic parameters measurement of Y-waveguide multifunction integrated optic circuit based on noise reduction processing

The polarization characteristics of the lithium niobate multifunction integrated optic circuit (MIOC), including its polarization extinction ratio (PER) and pigtail crosstalk, have a significant impact on the accuracy of fiber optic gyroscopes. A distributed polarization coupling (DPC) MIOC polarization parameter measurement method based on noise reduction processing is proposed. The proposed method aims to classify signal components decomposed by complete ensemble empirical decomposition with adaptive noise (CEEMDAN) by setting the threshold of the autocorrelation coefficient to filter out noise components. By setting the dynamic threshold and step size, DPC points are adaptively extracted from the denoised signal and superimposed to calculate the PER and pigtail crosstalk. Compared with empirical mode decomposition and ensemble empirical mode decomposition methods, the PER value obtained by the CEEMDAN method has the best consistency with the measured value of a commercial extinction ratio meter, the mean difference is 0.014 dB, and the pigtail crosstalk is −43.77 dB.