Search Results (9,739)

The low-cost Strapdown Inertial Navigation System (SINS)/Global Navigation Satellite System (GNSS) is widely used in autonomous vehicles for positioning and navigation. Initial alignment is a critical stage for SINS operations, and the alignment time and accuracy directly affect the SINS navigation performance. To address the issue that low-cost SINS/GNSS cannot effectively achieve rapid and high-accuracy alignment in complex environments that contain noise and external interference, an adaptive multiple backtracking robust alignment method is proposed. The sliding window that constructs observation and reference vectors is established, which effectively avoids the accumulation of sensor errors during the full integration process. A new observation vector based on the magnitude matching is then constructed to effectively reduce the effect of outliers on the alignment process. An adaptive multiple backtracking method is designed in which the window size can be dynamically adjusted based on the innovation gradient; thus, the alignment time can be significantly shortened. Furthermore, the modified variational Bayesian Kalman filter (VBKF) that accurately adjusts the measurement noise covariance matrix is proposed, and the Expectation–Maximization (EM) algorithm is employed to refine the prior parameter of the predicted error covariance matrix. Simulation and experimental results demonstrate that the proposed method significantly reduces alignment time and improves alignment accuracy. Taking heading error as the critical evaluation indicator, the proposed method achieves rapid alignment within 120 s and maintains a stable error below 1.2° after 80 s, yielding an improvement of over 63% compared to the backtracking-based Kalman filter (BKF) method and over 57% compared to the fuzzy adaptive KF (FAKF) method. Full article

To address the limitations of the existing cotton seedling growth prediction methods in field environments, specifically, poor representation of spatiotemporal features and low visual fidelity in texture rendering, this paper proposes an algorithm for the prediction of cotton seedling growth from images based on FCA-STNet. The model leverages historical sequences of cotton seedling RGB images to generate an image of the predicted growth at time t + 1 and extracts 37 phenotypic traits from the predicted image. A novel STNet structure is designed to enhance the representation of spatiotemporal dependencies, while an Adaptive Fine-Grained Channel Attention (FCA) module is integrated to capture both global and local feature information. This attention mechanism focuses on individual cotton plants and their textural characteristics, effectively reducing the interference from common field-related challenges such as insufficient lighting, leaf fluttering, and wind disturbances. The experimental results demonstrate that the predicted images achieved an MSE of 0.0086, MAE of 0.0321, SSIM of 0.8339, and PSNR of 20.7011 on the test set, representing improvements of 2.27%, 0.31%, 4.73%, and 11.20%, respectively, over the baseline STNet. The method outperforms several mainstream spatiotemporal prediction models. Furthermore, the majority of the predicted phenotypic traits exhibited correlations with actual measurements with coefficients above 0.8, indicating high prediction accuracy. The proposed FCA-STNet model enables visually realistic prediction of cotton seedling growth in open-field conditions, offering a new perspective for research in growth prediction. Full article

Icing on eight-bundled conductors can significantly alter their aerodynamic behavior, potentially leading to structural instabilities such as galloping. This study employed wind tunnel experiments and numerical simulations to analyze the aerodynamic parameters of each iced conductor across various angles of attack. The simulations incorporated detailed stranded conductor geometries to assess their influence on aerodynamic accuracy. Incorporating stranded geometry in simulations reduced average errors in lift and drag coefficients by 45–50% compared to smooth models. The Den Hartog coefficient prediction error decreased from 15.6% to 3.9%, indicating improved reliability in oscillation predictions. Additionally, conductors with larger windward areas exhibited more pronounced wake effects, with lower sub-conductors experiencing greater wake interference than upper ones. The above results illustrate that explicit modeling of stranded conductor surfaces enhances the precision of aerodynamic simulations, providing a more accurate framework for predicting icing-induced galloping in multi-bundled conductors. Full article

The demand for high data rates and large system capacity has posed significant challenges for medium access control (MAC) methods. Successive interference cancellation (SIC) is a classical multi-user detection (MUD) method; however, it suffers from an error propagation problem. To address this deficiency, we propose a method called Virtual Signal Processing-Based Integrated Multi-User Detection (VSP-IMUD). In VSP-IMUD, the received mixed multi-user signals are treated as an equivalent signal. The channel ambiguity corresponding to each user’s signal is then examined. For channels with non-zero ambiguity values, the signal components are detected using zero-forcing (ZF) reception. Next, the detected ambiguous signal components are reconstructed and subtracted from the received mixed signal using SIC. Once all the ambiguous signals are detected, the remaining signal components with zero ambiguity values are equated to a virtual integrated signal, to which a matched filter (MF) is applied. Finally, by selecting the signal with the highest channel gain and adopting its data as the reference symbol, the remaining signals’ dataset can be determined. Our theoretical analysis and simulation results demonstrate that VSP-IMUD effectively reduces the frequency of SIC applications and mitigates its error propagation effects, thereby improving the system’s bit-error rate (BER) performance. Full article

The aircraft nose-wheel steering system serves as a critical component for ensuring ground taxiing safety and maneuvering efficiency. However, its dynamic control stability faces significant challenges under complex operational conditions. Existing research predominantly focuses on single-discipline modeling, with insufficient in-depth analysis of the coupling effects between hydraulic system dynamics and mechanical dynamics. Traditional PID controllers exhibit limitations in scenarios involving nonlinear time-varying conditions caused by normal load fluctuations of the landing gear buffer strut during high-speed landing phases, including increased control overshoot and inadequate adaptability to abrupt load variations. These issues severely compromise the stability of high-speed deviation correction and overall aircraft safety. To address these challenges, this study constructs a digital twin model based on real aircraft data and innovatively implements multidisciplinary co-simulation via Simcenter 3D, AMESim 2021.1, and MATLAB R2020a. A fuzzy adaptive PID controller is specifically designed to achieve adaptive adjustment of control parameters. Comparative analysis through co-simulation demonstrates that the proposed mechanical–electrical–hydraulic collaborative control strategy significantly reduces response delay, effectively minimizes control overshoot, and decreases hydraulic pressure-fluctuation amplitude by over 85.2%. This work provides a novel methodology for optimizing steering stability under nonlinear interference scenarios, offering substantial engineering applicability and promotion value. Full article

Remote sensing images are often degraded by atmospheric haze, which not only reduces image quality but also complicates information extraction, particularly in high-level visual analysis tasks such as object detection and scene classification. State-space models (SSMs) have recently emerged as a powerful paradigm for vision tasks, showing great promise due to their computational efficiency and robust capacity to model global dependencies. However, most existing learning-based dehazing methods lack physical interpretability, leading to weak generalization. Furthermore, they typically rely on spatial features while neglecting crucial frequency domain information, resulting in incomplete feature representation. To address these challenges, we propose ScaleViM-PDD, a novel network that enhances an SSM backbone with two key innovations: a Multi-scale EfficientViM with Physical Decoupling (ScaleViM-P) module and a Dual-Domain Fusion (DD Fusion) module. The ScaleViM-P module synergistically integrates a Physical Decoupling block within a Multi-scale EfficientViM architecture. This design enables the network to mitigate haze interference in a physically grounded manner at each representational scale while simultaneously capturing global contextual information to adaptively handle complex haze distributions. To further address detail loss, the DD Fusion module replaces conventional skip connections by incorporating a novel Frequency Domain Module (FDM) alongside channel and position attention. This allows for a more effective fusion of spatial and frequency features, significantly improving the recovery of fine-grained details, including color and texture information. Extensive experiments on nine publicly available remote sensing datasets demonstrate that ScaleViM-PDD consistently surpasses state-of-the-art baselines in both qualitative and quantitative evaluations, highlighting its strong generalization ability. Full article

Synthetic aperture radar (SAR), as an active microwave imaging system, has the capability of all-weather and all-time observation. In response to the challenges of aircraft detection in SAR images due to the complex background interference caused by the continuous scattering of airport buildings and the demand for real-time processing, this paper proposes a YOLOv7-MTI recognition model that combines the attention mechanism and involution. By integrating the MTCN module and involution, performance is enhanced. The Multi-TASP-Conv network (MTCN) module aims to effectively extract low-level semantic and spatial information using a shared lightweight attention gate structure to achieve cross-dimensional interaction between “channels and space” with very few parameters, capturing the dependencies among multiple dimensions and improving feature representation ability. Involution helps the model adaptively adjust the weights of spatial positions through dynamic parameterized convolution kernels, strengthening the discrete strong scattering points specific to aircraft and suppressing the continuous scattering of the background, thereby alleviating the interference of complex backgrounds. Experiments on the SAR-AIRcraft-1.0 dataset, which includes seven categories such as A220, A320/321, A330, ARJ21, Boeing737, Boeing787, and others, show that the mAP and mRecall of YOLOv7-MTI reach 93.51% and 96.45%, respectively, outperforming Faster R-CNN, SSD, YOLOv5, YOLOv7, and YOLOv8. Compared with the basic YOLOv7, mAP is improved by 1.47%, mRecall by 1.64%, and FPS by 8.27%, achieving an effective balance between accuracy and speed, providing research ideas for SAR aircraft recognition. Full article

Background/Objectives: Viral respiratory infections have a significant impact on global health and the economy. While vaccines are effective in preventing infection, they might not be available or sufficient when used alone and must be complemented by specific therapeutic strategies. The development of new antiviral agents is increasingly important due to the continual emergence of novel respiratory pathogens. Previously we identified bovine lactoferrin (bLf)-derived tetrapeptides and peptidomimetics that showed potent in vitro activity against the influenza A virus in the picomolar range. Methods: Inspired by these results, in this study, we evaluated the antiviral potential of these compounds against HCoV-229E, a human coronavirus that can cause severe disease in immunocompromised individuals, using a compound repositioning approach. Results: Functional studies revealed that SK(N-Me)HS (3) interferes with viral entry and replication, while compound SNKHS (5) primarily blocks infection in the early stages. Biophysical analyses confirmed the occurrence of high-affinity binding to the viral spike protein, and computational studies suggested that the compounds target a region involved in conformational changes necessary for membrane fusion. Conclusions: These findings highlight these compounds as promising candidates for coronavirus entry inhibition and underscore the value of compound repurposing in antiviral development. Full article

This study aims to investigate the effects of phase synchronisation on tonal noise reduction in a multi-rotor UAV using an electronic phase-locking system. Experiments at the University of Bristol explored the impact of relative phase angle, propeller spacing, and blade geometry on acoustic performance, including psychoacoustic annoyance. Results show that increasing the phase angle consistently reduces the sound pressure level (SPL) due to destructive interference. For the two-bladed configuration, the highest noise reduction occurred at relative phase angle $\Delta \psi ={90}^{\circ }$, with a 19 dB decrease at the first blade-passing frequency (BPF). Propeller spacing had minimal impact when phase synchronisation was applied. The pitch-to-diameter ($P/D$) ratio also influenced results: for $P/D=0.55$, reductions ranged from 13–18 dB; and for $P/D=1.0$, reductions ranged from 10–20 dB. Maximum psychoacoustic annoyance was observed when propellers were in phase ($\Delta \psi =0^{\circ }$), while annoyance decreased with increasing phase angle, confirming the effectiveness of phase control for noise mitigation. For the five-bladed configuration, the highest reduction of 15 dB occurred at $\Delta \psi ={36}^{\circ }$, with annoyance levels also decreasing with phase offset. Full article

Schizophrenia is a challenging multifactorial neuropsychiatric disease that involves interactions between genetic susceptibility and environmental insults. Increasing evidence implicates viral infections as significant environmental contributors, particularly during sensitive neurodevelopmental periods. This review synthesises current findings on the viral hypothesis of schizophrenia, encompassing a wide array of neurotropic viruses, including influenza viruses, herpesviruses (HSV-1 and 2, CMV, VZV, EBV, HHV-6 and 8), hepatitis B and C viruses, HIV, HERVs, HTLV, Zika virus, BoDV, coronaviruses (including SARS-CoV-2), and others. These pathogens can contribute to schizophrenia through mechanisms such as direct microinvasion, persistent central nervous system infection, immune-mediated neuroinflammation, molecular mimicry, and the disturbance of the blood–brain barrier. Prenatal exposure to viral infections can trigger maternal immune activation, resulting in cytokine-mediated alterations in the neurological development of the foetus that persist into adulthood. Genetic studies highlight the role of immune-related loci, including major histocompatibility complex polymorphisms, in modulating susceptibility to infection and neurodevelopmental outcomes. Clinical data also support the “mild encephalitis” hypothesis, suggesting that a subset of schizophrenia cases involve low-grade chronic neuroinflammation. Although antipsychotics have some immunomodulatory effects, adjunctive anti-inflammatory therapies show promise, particularly in treatment-resistant cases. Despite compelling associations, pathogen-specific links remain inconsistent, emphasising the need for longitudinal studies and integrative approaches such as viromics to unravel causal relationships. This review supports a “multi-hit” model in which viral infections interfere with hereditary and immunological susceptibilities, enhancing schizophrenia risk. Elucidating these virus–immune–brain interactions may facilitate the discovery of biomarkers, targeted prevention, and novel therapeutic strategies for schizophrenia. Full article

The detection of glucose concentration has a wide range of applications and plays a significant role in the fields of the food industry, medical health, and illness diagnostics. The utilization of sensor technology for glucose concentration detection is an effective approach. Glucose sensors utilizing nanomaterials, with high sensitivity, strong resistance to interference, and compact size, exhibit tremendous potential in glucose concentration detection. Traditional enzyme-based sensors exhibit superior selectivity and high sensitivity; however, they are deficient in terms of interference resistance capabilities. With the development of nanotechnology, the performance of glucose sensors has been significantly improved. This review discusses the research progress in non-enzymatic electrochemical glucose nanosensors, including noble metal-based glucose sensors and non-noble transition metal compound-based glucose sensors, as well as the applications of multimetallic materials in nanosensors. Additionally, the application of nanosensors based on fluorescence and colorimetric principles in the detection of glucose concentration is introduced in this review. Finally, a perspective on the challenges and prospects of nanosensors in the field of glucose detection is presented. Full article

Previous studies observed that emotional scenes, presented as distractors, capture attention and interfere with an ongoing task. This behavioral interference has been shown to be elicited by the semantic rather than by the perceptual properties of a scene, as it resisted the application of low-pass spatial frequency filters. Some studies observed that the visual system can adapt to perceptual conditions; however, little is known concerning whether attentional capture by emotional stimuli can also be modulated by the sequential repetition of viewing conditions or of emotional content. In the present study, we asked participants to perform a parity task while viewing irrelevant natural scenes, which could be either emotional or neutral. These scenes could be either blurred (low-pass filter) or perceptually intact, and the order of presentation was balanced to study the effects of sequential repetition of perceptual conditions. The results indicate that affective modulation was most pronounced when the same viewing condition (either intact or blurred) was repeated, with faster responses when perceptual conditions were repeated for neutral distractors, but to a lesser extent for emotional ones. These data suggest that emotional interference in an attentional task can be modulated by serial sensitization in the processing of spatial frequencies. Full article

Background: Reliable platelet (PLT) measurement is crucial for the accurate diagnosis of thrombocytopenia. Several methods exist for automated PLT counting, including the impedance method (PLT-I), as well as optical and fluorescence methods (PLT-F). The impedance method is cost-effective but susceptible to interference from small red blood cells and schistocytes. In contrast, fluorescent assessment offers higher specificity but is more expensive, as it requires additional dyes and detectors. Hybrid platelet counting (PLT-H) combines impedance with measurements from the leukocyte differentiation channel and is available without additional cost. Aim: The aim of this study was to evaluate the accuracy of hybrid PLT counting in anemic samples. Methods: In this retrospective study, PLT counts from 583 unselected anemic samples were analyzed using two different analyzers: the Sysmex XN3500, equipped with fluorescent PLT-F technology, and the Mindray BC6200, which uses both impedance (PLT-I) and hybrid (PLT-H) technologies. Agreement between PLT-I and PLT-F, as well as between PLT-H and PLT-F, was assessed using Bland–Altman plots. Correlation between the methods was evaluated using the Pearson correlation coefficient. Results: The hybrid method demonstrated better accuracy in PLT counting compared to the impedance method. Correlation between PLT-H and PLT-F was excellent, ranging from 0.991 to 0.999. In thrombocytopenic samples (PLT < 50 G/L), the hybrid method also provided more reliable PLT counts than the impedance method, reducing the number of falsely elevated PLT results by nearly fivefold. Conclusions: Hybrid platelet counting yields more accurate results than the impedance method in anemic samples and shows excellent correlation with the fluorescence method. Full article

Focusing on the problem of insufficient detection accuracy caused by blurred target boundaries, variable scales, and severe noise interference in side-scan sonar images, this paper proposes a high-precision detection network named ESL-YOLO, which integrates edge perception and adaptive quality assessment. Firstly, an Edge Fusion Module (EFM) is designed, which integrates the Sobel operator into depthwise separable convolution. Through a dual-branch structure, it realizes effective fusion of edge features and spatial features, significantly enhancing the ability to recognize targets with blurred boundaries. Secondly, a Self-Calibrated Dual Attention (SCDA) Module is constructed. By means of feature cross-calibration and multi-scale channel attention fusion mechanisms, it achieves adaptive fusion of shallow details and deep-rooted semantic content, improving the detection accuracy for small-sized targets and targets with elaborate shapes. Finally, a Location Quality Estimator (LQE) is introduced, which quantifies localization quality using the statistical characteristics of bounding box distribution, effectively reducing false detections and missed detections. Experiments on the SIMD dataset show that the mAP@0.5 of ESL-YOLO reaches 84.65%. The precision and recall rate reach 87.67% and 75.63%, respectively. Generalization experiments on additional sonar datasets further validate the effectiveness of the proposed method across different data distributions and target types, providing an effective technical solution for side-scan sonar image target detection. Full article

To enhance the performance of UAVs in detecting insulator self-explosion defects during power inspections, this paper proposes an insulator self-explosion defect recognition algorithm, SDA-YOLO, based on an improved YOLOv11s network. First, the SODL is added to YOLOv11 to fuse shallow features with deeper features, thereby improving the model’s focus on small-sized self-explosion defect features. The OBB is also employed to reduce interference from the complex background. Second, the DBB module is incorporated into the C3k2 module in the backbone to extract target features through a multi-branch parallel convolutional structure. Finally, the AIFI module replaces the C2PSA module, effectively directing and aggregating information between channels to improve detection accuracy and inference speed. The experimental results show that the average accuracy of SDA-YOLO reaches 96.0%, which is higher than the YOLOv11s baseline model of 6.6%. While maintaining high accuracy, the inference speed of SDA-YOLO can reach 93.6 frames/s, which achieves the purpose of the real-time detection of insulator faults. Full article