Weak Signal Research Articles

Interpretable single-cell factor decomposition using sciRED.

Single-cell RNA sequencing (scRNA-seq) maps gene expression heterogeneity within a tissue. However, identifying biological signals in this data is challenging due to confounding technical factors, sparsity, and high dimensionality. Data factorization methods address this by separating and identifying signals in the data, such as gene expression programs, but the resulting factors must be manually interpreted. We developed Single-Cell Interpretable Residual Decomposition (sciRED) to improve the interpretation of scRNA-seq factor analysis. sciRED removes known confounding effects, uses rotations to improve factor interpretability, maps factors to known covariates, identifies unexplained factors that may capture hidden biological phenomena and determines the genes and biological processes represented by the resulting factors. We apply sciRED to multiple scRNA-seq datasets and identify sex-specific variation in a kidney map, discern strong and weak immune stimulation signals in a PBMC dataset, reduce ambient RNA contamination in a rat liver atlas to help identify strain variation, and reveal rare cell type signatures and anatomical zonation gene programs in a healthy human liver map. These demonstrate that sciRED is useful in characterizing diverse biological signals within scRNA-seq data.

Exploring Weak Magnetic Signal Characteristics of Pipeline Welds: Insights into Stress Non-Uniformity Effects.

Weak magnetic detection technology can detect stress concentration areas in ferromagnetic materials. However, the stress non-uniform characteristics of pipeline welds lead to significant differences in stress distribution range and values between inner wall welds and outer wall welds. This discrepancy makes it crucial to further evaluate the impact of stress non-uniformity on magnetic signals. To study the magnetic signal characteristics under the influence of residual stress in weld seams, a magneto-mechanical analytical model was established based on the magnetic charge theory and the distribution characteristics of residual stress in the weld seam. The magneto-mechanical relationship and magnetic signal distribution characteristics at the inner and outer wall welds of the pipeline are calculated. Furthermore, the effects of different excitation intensities on the amplitude growth characteristics of magnetic signals are analyzed and compared. To verify the analysis model, weld detection experiments with different excitation intensities were designed. The results show that both the peak-to-valley values of the normal component and the peak values of the tangential component of the outer wall weld are lower than those of the inner wall weld. Conversely, the peak-to-valley width of the normal component and the peak width of the tangential component are greater than those of the inner wall weld. Additionally, the rate of increase in weak magnetic signal amplitude decreases in a first-order exponential relationship with increasing excitation intensity. The average decay rates of the normal and tangential component amplitude growth rates for the inner wall weld are 34.03% and 27.9%, respectively, while for the outer wall weld, they are 31.75% and 28.01%, respectively. This study contributes to the identification and quantitative assessment of weak magnetic signals in inner and outer wall welds.

Extraction of Weak Harmonic Target Signal from Ionospheric Noise of High Frequency Surface Wave Radar

Extraction of Weak Harmonic Target Signal from Ionospheric Noise of High Frequency Surface Wave Radar

Enhancing GWOPS Capabilities for Coordinated Multi-Telescope Detection of Gravitational Wave Electromagnetic Counterparts

The groundbreaking detection of gravitational waves (GWs) has ushered in a new era of astronomical observation, granting us access to cosmic phenomena that are imperceptible to electromagnetic waves. The inherently weak GW signals coupled with the substantial uncertainties in source localization pose significant challenges to the field of astronomy. In this paper, we introduce innovative strategies to enhance the efficiency of observing electromagnetic counterparts to GW events, thereby unlocking further secrets of the cosmos. We present a novel technique for designing observation targets and establishing priorities, progressing from the epicenter to the periphery within the boundaries of the GW error sky region. This method has significantly reduced the average slewing distance of telescopes by 41% compared to traditional methods, thus enhancing observational efficiency. Additionally, we have developed a collaborative observation strategy for telescope networks, allocating observation targets based on the field-of-view (FOV) sizes of individual telescopes. This ensures comprehensive coverage without redundancy, allowing a network of four telescopes to cover a sky area and accumulate observation probability more than four times that of a single telescope operating independently over an equivalent period. Building upon these strategies, we have significantly upgraded GWOPS, the GW Follow-up Observation Planning System developed by the China-VO team, to provide precise observational planning for large FOV (greater than 1 square degree) telescope networks. The system also features a web-based user interface that presents the GW error sky area and observation planning results in a graphical format, significantly improving user interaction and experience. The research presented herein equips astronomers with a robust toolkit, advancing the efficiency of searching for and studying electromagnetic counterparts to GW events, and heralding new frontiers in the research of astrophysics and cosmology.

Silver nanogranules-decorated ZnO hybrid nanostructures with enhanced UV photoresponses

Detecting low-power ultraviolet (UV) light is crucial for practical applications. However, 1D ZnO/Ag based photodetectors (PDs) suffer from high expenses and care due to special operating conditions, posing challenges in capturing weak light signals and response time. To overcome these limitations, we integrate ZnO nanostructures with localized surface plasmon resonance of Ag nanoparticles via a surfactant-free hydrothermal-assisted polyol method, resulting in a notable bandgap reduction. SEM analysis revealed roughly spherical nanostructures for pristine ZnO and a porous structure in ZnO@Ag, around 1.4 µm in size with small agglomerates. XRD patterns showed reduced ZnO diffraction peak intensities in ZnO@Ag, confirming their core-like ZnO morphology. The fabricated ZnO@Ag heterojunction PD on an FTO substrate resulted in notable enhancement of PD performance. The ZnO@Ag heterojunction PD demonstrated a rapid 1.77 s response time, a 0.64 s recovery time and a specific detectivity of 11×107 cmHz1/2W−1, indicating its potential for low-cost and sustainable optoelectronics.

Ultranarrow and multiband perfect absorbers based on dual quasi-bound states in the continuum in near infrared

High quality factor (Q-factor) and multiband perfect absorbers are highly desired in light-harvesting efficiency related optical devices, yet they are always limited by conventional mechanisms like electromagnetically induced transparency, Tamm or plasmonic resonance. In this paper, through constructing the strong coupling between two guided-mode resonances in subwavelength gratings, we propose an ultranarrow and multiband perfect absorber based the dual-band quasi-bound states in the continuum (quasi-BIC) design in near infrared. Benefiting from its angle-dependent characteristic, the device can be straightforwardly utilized for light-switched lamps and optical sensors. Q-factor 8337 and the modulation depth 98.1% are realized while the insertion loss is only 0.07 dB. Compared with its conventional counterparts, the quasi-BIC based absorber enables an excellent hexa-frequency and ultranarrow asynchronous switch function. The quatary-band perfect absorption below 1 nm bandwidth can be applied to the optical sensor to detect four light signals with weak signal strength. This work provides a theoretical basis for multifunctional absorbers with high Q-factor, high modulation depth, and low insertion loss.

Incorporating prior information in gene expression network-based cancer heterogeneity analysis.

Cancer is molecularly heterogeneous, with seemingly similar patients having different molecular landscapes and accordingly different clinical behaviors. In recent studies, gene expression networks have been shown as more effective/informative for cancer heterogeneity analysis than some simpler measures. Gene interconnections can be classified as "direct" and "indirect," where the latter can be caused by shared genomic regulators (such as transcription factors, microRNAs, and other regulatory molecules) and other mechanisms. It has been suggested that incorporating the regulators of gene expressions in network analysis and focusing on the direct interconnections can lead to a deeper understanding of the more essential gene interconnections. Such analysis can be seriously challenged by the large number of parameters (jointly caused by network analysis, incorporation of regulators, and heterogeneity) and often weak signals. To effectively tackle this problem, we propose incorporating prior information contained in the published literature. A key challenge is that such prior information can be partial or even wrong. We develop a two-step procedure that can flexibly accommodate different levels of prior information quality. Simulation demonstrates the effectiveness of the proposed approach and its superiority over relevant competitors. In the analysis of a breast cancer dataset, findings different from the alternatives are made, and the identified sample subgroups have important clinical differences.

Electric shock fault identification method based on DWT-AE-BPNN for residual current devices in power distribution systems

The protection dead-zone and threshold setting difficulties of the residual current devices (RCDs) in low-voltage distribution networks may lead to the misidentification of electric shock fault, resulting in severe life-threatening accidents. This paper proposes an electric shock fault identification method based on artificial intelligence for RCDs. Firstly, Mallat discrete wavelet transform (DWT) is applied to efficiently extract non-stationary electric shock feature signals from the total residual current with various noises, preventing weak non-stationary electric shock feature signals from being filtered out. Based on the average and maximum components of the signal mutation, an adaptive threshold can be determined to detect electric shock accurately, avoiding the false activation of RCDs caused by load fluctuations. Subsequently, an autoencoder (AE) is built to mine the non-linear features in which the signal of electric shock on living gradually rises and the signal of electric shock on non-living remains stable. Finally, a back propagation neural network (BPNN) is trained to classify the electric shock types from the non-linear features. The simulation and experiment have been conducted to obtain total residual current data under different conditions, and the electric shock fault real-time identification hardware platforms are developed. The accuracy of electric shock fault detection and classification can reach 100 %, which has advanced its practical applicability.

How universities study the future: A critical view

Despite recent rapid advances in technology, a skilled workforce continues to play the most important role in the development of national economies. People’s roles as operators, managers, or innovators are rapidly evolving to meet changing social needs. Traditionally, universities and higher education (HE) systems have been responsible for supplying the required knowledge and human resources. However, in the modern world, the nature of these needs is constantly changing, and uncertainties are dominating the socioeconomic future. To address this need, many researchers have attempted to explore how HE can cope with future uncertainties and evolve as a social system, yet there is no comprehensive analysis of methodological guidelines that provide insights into how to develop HE future scenarios. To fill this gap, this research takes a deep literature review approach, conducted by the authors, to examine published and peer-reviewed studies in the area of HE scenario planning over the decade from January 2010 to 2023. This study investigates the exploration of plausible futures by research teams at three levels - global/international, regional/national, and local/university. The findings show that there is a lack of foresight knowledge, which increases the risk of failure exponentially over time. To address these shortcomings, this study proposes a framework for future studies, including environmental factors that will impact higher education and methodological recommendations to equip researchers for the challenges ahead. It also emphasizes the need to appreciate the complexity of HE systems and the role of wildcards and weak signals in shaping future disruptive trends. This research offers valuable guidance to researchers seeking to engage in scenario development for HE in the face of uncertainty.

Mechanism of strain-induced magnetic signal change in ferromagnetic materials under weak magnetic environment

Weak magnetic detection based on force-magnetic coupling effect is widely used in nondestructive testing of ferromagnetic materials, but the weak magnetic signals caused by the deformation of ferromagnetic materials under the action of external load show nonlinear changes, and the existing theories can not make a unified conclusion on the mechanism of nonlinear changes of magnetic signals, which limits the further application of weak magnetic detection. In this paper, a microscopic model of ferromagnetic material is established with the strain of the material as the independent variable. Firstly, the reasonableness of the established model is verified by the stress–strain relationship, and the stress concentration phenomenon generated by the change of the microscopic structure of the model is studied in depth by using the XRD diffraction method, so as to put forward the idea of characterizing the macroscopic stress with microscopic dislocations; secondly, the mechanism of the change of the magnetic signals of the ferromagnetic material caused by strain is analyzed by using the atomic magnetic moments and the density of states. The model explains the mechanism of strain-induced magnetic signal change in ferromagnetic materials, and has a guiding role in the further application and promotion of the weak magnetic method.

Multipressure Sampling for Improving the Performance of MOF-based Electronic Noses.

Metal-organic frameworks (MOFs) are a promising class of porous materials for the design of gas sensing arrays, which are often called electronic noses. Due to their chemical and structural tunability, MOFs are a highly diverse class of materials that align well with the similarly diverse class of volatile organic compounds (VOCs) of interest in many gas detection applications. In principle, by choosing the right combination of cross-sensitive MOFs, layered on appropriate signal transducers, one can design an array that yields detailed information about the composition of a complex gas mixture. However, despite the vast number of MOFs from which one can choose, gas sensing arrays that rely too heavily on distinct chemistries can be impractical from the cost and complexity perspective. On the other hand, it is difficult for small arrays to have the desired selectivity and sensitivity for challenging sensing applications, such as detecting weakly adsorbing gases with weak signals, or conversely, strongly adsorbing gases that readily saturate MOF pores. In this work, we employed gas adsorption simulations to explore the use of a variable pressure sensing array as a means of improving both sensitivity and selectivity as well as increasing the information content provided by each array. We studied nine different MOFs (HKUST-1, IRMOF-1, MgMOF-74, MOF-177, MOF-801, NU-100, NU-125, UiO-66, and ZIF-8) and four different gas mixtures, each containing nitrogen, oxygen, carbon dioxide, and exactly one of the hydrogen, methane, hydrogen sulfide, or benzene. We found that by lowering the pressure, we can limit the saturation of MOFs, and by raising the pressure, we can concentrate weakly adsorbing gases, in both cases, improving gas detection with the resulting arrays. In many cases, changing the system pressure yielded a better improvement in performance (as measured by the Kullback-Liebler divergence of gas composition probability distributions) than including additional MOFs. We thus demonstrated and quantified how sensing at multiple pressures can increase information content and cross-sensitivity in MOF-based arrays while limiting the number of unique materials needed in the device.

Hybrid Integrated Wearable Patch for Brain EEG-fNIRS Monitoring.

Synchronous monitoring electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have received significant attention in brain science research for their provision of more information on neuro-loop interactions. There is a need for an integrated hybrid EEG-fNIRS patch to synchronously monitor surface EEG and deep brain fNIRS signals. Here, we developed a hybrid EEG-fNIRS patch capable of acquiring high-quality, co-located EEG and fNIRS signals. This patch is wearable and provides easy cognition and emotion detection, while reducing the spatial interference and signal crosstalk by integration, which leads to high spatial-temporal correspondence and signal quality. The modular design of the EEG-fNIRS acquisition unit and optimized mechanical design enables the patch to obtain EEG and fNIRS signals at the same location and eliminates spatial interference. The EEG pre-amplifier on the electrode side effectively improves the acquisition of weak EEG signals and significantly reduces input noise to 0.9 μVrms, amplitude distortion to less than 2%, and frequency distortion to less than 1%. Detrending, motion correction algorithms, and band-pass filtering were used to remove physiological noise, baseline drift, and motion artifacts from the fNIRS signal. A high fNIRS source switching frequency configuration above 100 Hz improves crosstalk suppression between fNIRS and EEG signals. The Stroop task was carried out to verify its performance; the patch can acquire event-related potentials and hemodynamic information associated with cognition in the prefrontal area.

A high-dimensional omnibus test for set-based association analysis.

Set-based association analysis is a valuable tool in studying the etiology of complex diseases in genome-wide association studies, as it allows for the joint testing of variants in a region or group. Two common types of single nucleotide polymorphism (SNP)-disease functional models are recognized when evaluating the joint function of a set of SNP: the cumulative weak signal model, in which multiple functional variants with small effects contribute to disease risk, and the dominating strong signal model, in which a few functional variants with large effects contribute to disease risk. However, existing methods have two main limitations that reduce their power. Firstly, they typically only consider one disease-SNP association model, which can result in significant power loss if the model is misspecified. Secondly, they do not account for the high-dimensional nature of SNPs, leading to low power or high false positives. In this study, we propose a solution to these challenges by using a high-dimensional inference procedure that involves simultaneously fitting many SNPs in a regression model. We also propose an omnibus testing procedure that employs a robust and powerful P-value combination method to enhance the power of SNP-set association. Our results from extensive simulation studies and a real data analysis demonstrate that our set-based high-dimensional inference strategy is both flexible and computationally efficient and can substantially improve the power of SNP-set association analysis. Application to a real dataset further demonstrates the utility of the testing strategy.

Enhanced detection of UV fluorescence from food products and tissue using lanthanide-doped polymer composite membranes

We demonstrate polymer composite membranes that can be coupled to conventional spectroscopic tools for enhancing the detection of weak UV fluorescence signals. The membranes incorporate a down-conversion phosphor with lanthanide ions allowing for the detection of UV emission from biomolecules commonly found in food products. We also explore their capability to discern between healthy and wounded tissue through measurements of the UV autofluorescence emission from skin. Our results show that the composite membranes have great potential to improve UV fluorescence detection in applications related to the biomedical, pharmaceutical and food areas.

Fourth-stable stochastic resonance and its application to weak fault signal detection of rotating machinery

Weak characteristic extraction is vital for weak fault signal detection of machinery. Stochastic resonance (SR) is able to transfer noise energy into weak fault characteristic frequency excited by a defect of machines. However, the potential function in SR is vital to enhance weak fault characteristic frequency and determines the capability of SR to improve the signal-to-noise ratio (SNR) of a noisy signal. Now, common potential functions include monostable, bistable and even tri-stable potentials but fourth-stable SR has not been studied and applied to detect early fault characteristic frequency. In this paper, thus, we would investigate the behaviors of SR with a fourth-stable potential subject to additive noise, in which the approximate theoretical expression of the power done by SR is derived to demonstrate the fourth-stable Sr Then, a SR method with the fourth-stable potential is proposed to enhance weak fault characteristic frequency, in which these system parameters are adjusted by using SNR as the objective function and using genetic algorithms adaptively. In this paper, thus, Finally, the proposed method is verified by using a simulated signal with noise and two early fault experiment of rolling element bearings with different levels of defects on the outer and inner races. Moreover, the proposed method is compared with wavelet denoising and fast kurtogram methods. The comparisons indicate that the proposed method has the better performance for enhancing weak fault characteristic frequency or weak useful signals than other two methods and is available to weak fault signal detection of machinery.

Cardiovascular Risk of Opioids: A Real-World Study Based on FAERS

Objective: This research utilizes the FAERS for data mining to identify heart-related side effects caused by opioids, ensuring the safe use of these medications. Methods: Data from 79 quarters (Q1 2004 to Q3 2023) involving adverse event (AE) reports for opioids like morphine and oxycodone was reviewed. We applied the MedDRA system to categorize events and used statistical tools, ROR and BCPNN, for signal detection. These findings were cross-checked with drug labels and SIDER 4.1 for accuracy. Identified risks were then categorized by severity using DME and IME classifications. Results: Analysis of adverse events (AEs) for the five examined drugs (35359, 14367, 144441, 10592, and 28848) identified 33, 6, 12, 37, and 34 cardiovascular AEs, and 16, 5, 7, 25, and 21 instances of important medical events (IMEs) respectively. Each drug was linked to cases of cardiac and cardiopulmonary arrest. The cardiovascular AEs varied widely in occurrence and severity, with methadone notably presenting diverse and potent risks, including sudden cardiac death as a distinct medical event (DME). A comparison with SIDER 4.1 showed 11 opioid-related cardiovascular AEs in line with our findings. Standardized MedDRA Queries (SMQs) confirmed these results, indicating stronger signals for methadone and tramadol, while morphine, hydromorphone, and oxycodone exhibited fewer and weaker signals. Conclusion: The study revealed numerous heart-related adverse effects (AEs) not listed on drug labels and identified new AE patterns. Recognizing these differences in AE profiles and risks across different opioids is crucial for safer prescription practices to minimize cardiac complications.

Rapid Initialization Method of Unmanned Aerial Vehicle Swarm Based on VIO-UWB in Satellite Denial Environment

In environments where satellite signals are blocked, initializing UAV swarms quickly is a technical challenge, especially indoors or in areas with weak satellite signals, making it difficult to establish the relative position of the swarm. Two common methods for initialization are using the camera for joint SLAM initialization, which increases communication burden due to image feature point analysis, and obtaining a rough positional relationship using prior information through a device such as a magnetic compass, which lacks accuracy. In recent years, visual–inertial odometry (VIO) technology has significantly progressed, providing new solutions. With improved computing power and enhanced VIO accuracy, it is now possible to establish the relative position relationship through the movement of drones. This paper proposes a two-stage robust initialization method for swarms of more than four UAVs, suitable for larger-scale satellite denial scenarios. Firstly, the paper analyzes the Cramér–Rao lower bound (CRLB) problem and the moving configuration problem of the cluster to determine the optimal anchor node for the algorithm. Subsequently, a strategy is used to screen anchor nodes that are close to the lower bound of CRLB, and an optimization problem is constructed to solve the position relationship between anchor nodes through the relative motion and ranging relationship between UAVs. This optimization problem includes quadratic constraints as well as linear constraints and is a quadratically constrained quadratic programming problem (QCQP) with high robustness and high precision. After addressing the anchor node problem, this paper simplifies and improves a fast swarm cooperative positioning algorithm, which is faster than the traditional multidimensional scaling (MDS) algorithm. The results of theoretical simulations and actual UAV tests demonstrate that the proposed algorithm is advanced, superior, and effectively solves the UAV swarm initialization problem under the condition of a satellite signal rejection.

Genetic algorithm optimized microstructure to enhance waveguide light coupling efficiency at normal incidences

The recent trend to place well-designed photonic structures on waveguides is capable of effectively enhancing waveguides properties. One typical example is a nanostructure-empowered waveguide targeted for efficient light coupling. However, conceiving the high-freedom structures is a time-consuming and labor-intensive process, where an ineffective workflow limits the development of photonic microstructures. To address this issue, we deploy a genetic algorithm to customize structures in order to improve the coupling coefficients under predetermined situations (i.e., normal incidence combined with two polarizations). Three types of micropatterns are first conceived in the periodical model and then fully characterized on the real waveguide sizes. The simulated data reveal that the 550–1650 nm average coupling efficiencies of structure-enabled waveguides are raised by about 2% in contrast to the bare case, and the lineshapes are also flattened thanks to the grating modifications. In short, our solution underlines the role of an algorithm-developed nanostructure to lift waveguide coupling coefficients. By integrating well-engineering patterns, the waveguide-based probes may find a multitude of usages for weak signal detection and communication systems.

A novel comprehensive method for weak signal blind detection based on adaptive quasi-periodic potential stochastic resonance and its application in bearing fault detection

In order to solve the common output saturation of stochastic resonance systems and the limitation of classical index SNR for blind detection, a novel adaptive quasi-periodic potential stochastic resonance blind detection method is proposed. First, a model of quasi-periodic potential stochastic resonance (QPPSR) possessing infinite steady state is constructed and analyzed for its structure change pattern. The superior performance of the model is verified by using the fourth-order Runge–Kutta algorithm. Secondly, the mechanism of QPPSR is analyzed using the probability flow method, which reveals the relationship between system parameters and performance. Again, a novel comprehensive blind detection index (CBDI) is exquisitely constructed to make up for the shortcomings of each indicator. Finally, CBDI and QPPSR are constructed into an adaptive blind detection system and applied to bearing fault detection. The results analyzed by experiments verify the good engineering application prospect of CBDI-QPPSR.

A transcranial multiple waves suppression method for plane wave imaging based on Radon transform

Transcranial ultrasound imaging presents a significant challenge due to the intricate interplay between ultrasound waves and the heterogeneous human skull. The skull’s presence induces distortion, refraction, multiple scattering, and reflection of ultrasound signals, thereby complicating the acquisition of high-quality images. Extracting reflections from the entire waveform is crucial yet exceedingly challenging, as intracranial reflections are often obscured by strong amplitude direct waves and multiple scattering. In this paper, a multiple wave suppression method for ultrasound plane wave imaging is proposed to mitigate the impact of skull interference. Drawing upon prior research, we developed an enhanced high-resolution linear Radon transform using the maximum entropy principle and Bayesian method, facilitating wavefield separation. We detailed the process of wave field separation in the Radon domain through simulation of a model with a high velocity layer. When plane waves emitted at any steering angles, both multiple waves and first arrival waves manifested as distinct energy points. In the brain simulation, we contrasted the characteristic differences between skull reflection and brain-internal signal in Radon domain, and demonstrated that multiples suppression method reduces side and grating lobe levels by approximately 30 dB. Finally, we executed in vitro experiments using a monkey skull to separate weak intracranial reflection signals from strong skull reflections, enhancing the contrast-to-noise ratio by 85 % compared to conventional method using full waveform. This study deeply explores the effect of multiples on effective signal separation, addresses the complexity of wavefield separation, and verifies its efficacy through imaging, thereby significantly advancing ultrasound transcranial imaging techniques.