Search Results (941)

Steady-state visual evoked potentials (SSVEPs) are one of the key paradigms used in brain–computer interface (BCI) systems. Their performance, however, is substantially degraded by EEG artifacts of muscular, motion-related, and ocular origin. This issue is particularly pronounced in individuals exhibiting increased facial muscle tension or involuntary eye movements. The aim of this study was to develop and evaluate an EEG artifact reduction method based on auxiliary channels, including central (Cz), frontal (Fp1), electrooculographic (HEOG), and muscular electrodes (neck, cheek, jaw). Signals from these channels were used to model the physical sources of interference recorded concurrently with occipital brain activity (O1, O2, Oz). EEG signal cleaning was performed using linear regression in 1-s windows, followed by frequency-domain analysis to extract features related to stimulation frequencies and SSVEP classification using SVM and CNN algorithms. The experiment involved three visual stimulation frequencies (7, 8, and 9 Hz) generated by LEDs and the recording of controlled facial and jaw-related artifacts. Experiments conducted on 12 participants demonstrated a 9% increase in classification accuracy after artifact removal. Further analysis indicated that the Cz and jaw channels contributed most significantly to effective artifact suppression. The results confirm that the use of auxiliary channels substantially improves EEG signal quality and enhances the reliability of BCI systems under real-world conditions. Full article

This paper proposes a novel photonic crystal fiber-based 1 × 3 optical drop multiplexer design. According to numerical simulations, optical signals can be injected on the left core and divided into another core at various distances to separate the optical signals in a photonic crystal fiber structure. Throughout the length of the fiber, the innovative design controls the direction of light transmission between layers by alternating between multiple air-hole positions using pure silica layers. The optical systemic communications industry cannot function without wavelength multiplexers/demultiplexers. They function as a data combiner/separator. By employing an optical add-drop multiplexer, it becomes possible to add or remove signals from a stream of multiplexed signals without the need to be concerned about any potential interference with the existing signals, even when they are traveling at varying on-axis distances. This study provides findings about small optical drop multiplexers for fiber-to-the-home applications employing photonic crystal fiber at wavelengths of 0.85, 1.45, and 1.2 µm. Full article

With the rapid development and widespread application of multibeam echo-sounding systems, large-scale and high-resolution seafloor topography can be efficiently acquired, enabling precise mapping of seabed terrain. However, due to complex oceanographic conditions, instrumental noise, and acoustic interferences, the acquired multibeam data often contain outliers that deviate from the true seafloor surface. These outliers can distort the representation of seafloor topography, adversely affecting subsequent geological analysis and engineering applications. To address this issue, a hybrid outlier detection method combining CUBE filtering with the Isolation Forest (IForest) algorithm, termed CUBE-IForest, is proposed. The method first employs CUBE filtering to remove gross outliers based on local uncertainty estimation, followed by the application of IForest to identify subtle anomalies in the refined data, achieving hierarchical detection of outliers. Experimental results based on in situ multibeam bathymetric data from the northeastern Pacific demonstrate that compared with traditional filtering methods the CUBE-IForest approach significantly improves detection accuracy and reduces both false positive and false negative rates by approximately 30%, confirming its efficiency and reliability in seafloor mapping and analysis. Full article

At present, most of the switchgear partial discharge detection means are offline detection and cannot monitor multiple partial discharge sources online at the same time. Based on this, this paper investigates the application of ultrasonic technology in localized discharge fault localization in high-voltage switchgear, removes the background noise of localized discharge in switchgear by using soft and hard filtering; proposes a generalized cubic correlation algorithm on the basis of TODA, improves the accuracy of the time difference acquisition in the case of low signal-to-noise ratio; determines the number of multiple localized discharging power sources by using the single-channel signal blind source separation technique and singularity spectral analysis; and determines the number of multiple localized discharging power sources by using independent component analysis to separate them. As well as for the problem that TDOA cannot be directly applied to the localization of multiple partial discharge sources, independent component analysis is used to separate the mixed signals, and the disordered coordinate selection method is proposed to determine the coordinates of multiple partial discharge sources. The experimental results show that (1) the noise reduction method is able to remove the excess interference while preserving the localized discharge signals; (2) the improved generalized cubic inter-correlation algorithm is more resistant to interference and has less error than other time delay estimation algorithms. The localization error is reduced by 60 mm~68 mm compared to the basic correlation algorithm, 41 mm~47 mm compared to the twice correlation algorithm, and 17 mm~20 mm compared to the three times correlation algorithm, which is a big improvement compared to the pre-improved algorithm. (3) It is able to locate the multiple localized power sources, and the accuracy of the number of localized power sources reaches 88%. Full article

Electrochemical advanced oxidation processes based on peroxymonosulfate (PMS-EAOPs) have shown great promise for eliminating organic pollutants from water. However, earlier research primarily concentrated on pollutant degradation at the cathode, with little attention given to the anode’s role in PMS-EAOPs. In this work, we developed a PMS-EAOP system using nitrogen-doped carbon nanotubes (N-CNTs) as the electrocatalyst and examined the degradation of pollutants (acetamiprid (ATP) and sulfamethoxazole (SMX)) at both the cathode and anode. Our findings indicate that SMX was rapidly degraded at both electrodes, while ATP was effectively broken down only at the cathode, demonstrating the selective nature of PMS-EAOP. At a voltage of −2 V and 2.5 mM PMS, the pseudo-first-order rate constant (k_obs) for ATP at the cathode reached 0.122 min⁻¹, with over 92% removal within 30 min. In contrast, the anode exhibited high selectivity, removing ~75% of SMX (k_obs = 0.041 min⁻¹) while less than 20% of ATP was degraded. Analysis of reactive oxygen species showed that hydroxyl and sulfate radicals were produced and contributed to pollutant degradation at the cathode. In contrast, selective oxidation occurred at the anode, likely driven by direct electrolysis-induced nonradical oxidation responsible for the selective degradation. Phosphates and bicarbonates significantly inhibited the degradation of pollutants in the PMS-EAOP process (31.7–76.4%). In contrast, chloride ions exhibited an electrode-dependent effect, with the anode being less susceptible to interference from common water anions. Overall, this study highlights that while PMS-EAOP can selectively remove contaminants, the influence of water matrix components must be taken into account when treating real wastewater. Full article

Land seismic data are always contaminated by surface waves, which demonstrate strong energy, low velocity, and long vibrations. Such noises often mask deep effective reflections, seriously reducing the data’s signal-to-noise ratio while limiting the imaging accuracy of complex deep structures and the efficiency of hydrocarbon reservoir identification. To address this critical technical bottleneck, this paper proposes a surface wave joint reconstruction method based on stationary phase analysis, combining the cross-correlation seismic interferometry method with the convolutional seismic interferometry method. This approach integrates cross-correlation and convolutional seismic interferometry techniques to achieve coordinated reconstruction of surface waves in both shot and receiver domains while introducing adaptive weight factors to optimize the reconstruction process and reduce interference from erroneous data. As a purely data-driven framework, this method does not rely on underground medium velocity models, achieving efficient noise reduction by adaptively removing reconstructed surface waves through multi-channel matched filtering. Application validation with field seismic data from the piedmont regions of western China demonstrates that this method effectively suppresses high-energy surface waves, significantly restores effective signals, improves the signal-to-noise ratio of seismic data, and greatly enhances the clarity of coherent events in stacked profiles. This study provides a reliable technical approach for noise reduction in seismic data under complex near-surface conditions, particularly suitable for hydrocarbon exploration in regions with developed scattering sources such as mountainous areas in western China. It holds significant practical application value and broad dissemination potential for advancing deep hydrocarbon resource exploration and improving the quality of complex structural imaging. Full article

Glass wool waste constitutes a rapidly increasing fraction of construction and demolition residues, yet it remains one of the most challenging insulation materials to recycle. Its non-combustible nature, extremely low bulk density, and high fibre elasticity preclude energy recovery and severely limit conventional mechanical recycling routes, resulting in long-term landfilling and loss of mineral resources. Converting glass wool waste into a fine mineral powder represents a potentially viable pathway for its integration into low-carbon construction materials, provided that industrial scalability, particle-size control, and chemical compatibility with cementitious binders are ensured. This study investigates the industrial-scale milling of end-of-life glass wool waste in a ventilated horizontal ball mill. It compares two grinding routes: a corundum-free route (BK) and an abrasive-assisted route (ZK) employing α-Al₂O₃ corundum to intensify fibre fragmentation. Particle size distribution was quantified by laser diffraction using cumulative and differential analyses, as well as characteristic diameters. The results confirm that abrasive-assisted milling significantly enhances fragmentation efficiency and reduces the coarse fibre fraction. However, the study demonstrates that this gain in fineness is inherently coupled with the incorporation of α-Al₂O₃ into the milled powder, introducing a chemically foreign crystalline phase that cannot be removed by post-processing. From a cement-oriented perspective, this contamination represents a critical limitation, as α-Al₂O₃ may interfere with hydration reactions, aluminate–sulfate equilibria, and microstructural development in Portland and calcium sulfoaluminate binders. In contrast, the corundum-free milling route yields a slightly coarser, chemically unmodified powder, offering improved process robustness, lower operational complexity, and greater compatibility with circular economy objectives. The study establishes that, for the circular reuse of fibrous insulation waste in cementitious systems, particle fineness alone is insufficient as an optimization criterion. Instead, the combined consideration of fineness, chemical purity, and binder compatibility governs the realistic and sustainable reuse potential of recycled glass wool powders. Full article

The removal of protein-bound uremic toxins (PBUTs) from the blood of kidney failure patients with conventional dialysis is limited. However, as their harmful effects and association with morbidity and mortality in dialysis patients are increasingly recognized, PBUTs have become important therapeutic targets. In this review, PBUT removal with current state-of-the-art dialysis technologies and future perspectives are discussed. Strategies to enhance PBUT clearance include methods that interfere with PBUT–albumin binding, such as chemical displacers, high ionic strength, pH changes, or electromagnetic fields, thereby increasing the free fraction available for dialysis. While these methods have shown promise in vitro, and some also in vivo, long-term safety data are lacking. PBUT removal can also be increased by adsorption, either directly via hemoperfusion, or indirectly, e.g., via sorbents incorporated in a mixed-matrix membrane or dissolved in the dialysate. In the kidney, PBUTs are secreted in the proximal tubules; hence, a cell-based bioartificial kidney (BAK) that secretes PBUTs is proposed as an add-on to current dialysis. Yet both PBUT adsorption strategies and, in particular, BAKs face considerable challenges in upscaling and mass production at acceptable costs. In conclusion, many novel technologies are under development, all requiring further (pre)clinical testing and upscaling before these strategies can be applied in the clinic. Full article

Sensor degradation poses a significant challenge in autonomous driving. During heavy rainfall, interference from raindrops can adversely affect the quality of LiDAR point clouds, resulting in, for instance, inaccurate point measurements. This, in turn, can potentially lead to safety concerns if autonomous driving systems are not weather-aware, i.e., if they are unable to discern such changes. In this study, we release a new, large-scale, multi-modal emulated rain dataset, REHEARSE-3D, to promote research advancements in 3D point cloud de-raining. Distinct from the most relevant competitors, our dataset is unique in several respects. First, it is the largest point-wise annotated dataset (9.2 billion annotated points), and second, it is the only one with high-resolution LiDAR data (LiDAR-256) enriched with 4D RADAR point clouds logged in both daytime and nighttime conditions in a controlled weather environment. Furthermore, REHEARSE-3D involves rain-characteristic information, which is of significant value not only for sensor noise modeling but also for analyzing the impact of weather at the point level. Leveraging REHEARSE-3D, we benchmark raindrop detection and removal in fused LiDAR and 4D RADAR point clouds. Our comprehensive study further evaluates the performance of various statistical and deep learning models, where SalsaNext and 3D-OutDet achieve above $94\%$ IoU for raindrop detection. Full article

High-rise building facades are prone to defects due to prolonged exposure to complex environments. Infrared detection, as a commonly employed method for facade defect inspection, often results in low accuracy owing to abundant interferences and blurred defect boundaries. In this work, an intelligent defect detection method for high-rise building facades is proposed. In the first stage of the proposed method, a segmentation model based on DeepLabV3+ is proposed to remove interferences in infrared images using masks. The model incorporates a Post-Decoder Dual-Branch Boundary Refinement Module, which is subdivided into a boundary feature optimization branch and a boundary-guided attention branch. Sub-pixel-level contour refinement and boundary-adaptive weighting are hence achieved to mitigate edge blurring induced by thermal diffusion and to enhance the perception of slender cracks and cavity edges. A triple constraint mechanism is also introduced, combining cross-entropy, multi-scale Dice, and boundary-aware losses to address class imbalance and enhance segmentation performance for small targets. Furthermore, superpixel linear iterative clustering (SLIC) is utilized to enforce regional consistency, hence improving the smoothness and robustness of predictions. In the second stage of the proposed method, a defect detection model based on YOLOV11 is proposed to process masked infrared images for detecting hollow, seepage, cracks and detachment. This work validates the proposed method using 180 infrared images collected via unmanned aerial vehicles. The experimental results demonstrate that the proposed method achieves a detection precision of 89.7%, an mAP@0.5 of 87.9%, and a 57.8 mAP@50-95. surpassing other algorithms and confirming its effectiveness and superiority. Full article

Detecting Escherichia coli on food preparation and processing surfaces is critical for ensuring food safety and preventing foodborne illness. This study focuses on detecting E. coli contamination on common food processing surfaces using UV-C fluorescence imaging and deep learning. Four concentrations of E. coli (0, 10⁵, 10⁷, and 10⁸ colony forming units (CFU)/mL) and two egg solutions (white and yolk) were applied to stainless steel and white rubber to simulate realistic contamination with organic interference. For each concentration level, 256 droplets were inoculated in 16 groups, and fluorescence videos were captured. Droplet regions were extracted from the video frames, subdivided into quadrants, and augmented to generate a robust dataset, ensuring 3–4 droplets per sample. Wavelet-based denoising further improved image quality, with Haar wavelets producing the highest Peak Signal-to-Noise Ratio (PSNR) values, up to 51.0 dB on white rubber and 48.2 dB on stainless steel. Using this dataset, multiple deep learning (DL) models, including ConvNeXtBase, EfficientNetV2L, and five YOLO11-cls variants, were trained to classify E. coli concentration levels. Additionally, Eigen-CAM heatmaps were used to visualize model attention to bacterial fluorescence regions. Across four dataset groupings, YOLO11-cls models achieved consistently high performance, with peak test accuracies of 100% on white rubber and 99.60% on stainless steel, even in the presence of egg substances. YOLO11s-cls provided the best balance of accuracy (up to 98.88%) and inference speed (4–5 ms) whilst having a compact size (11 MB), outperforming larger models such as EfficientNetV2L. Classical machine learning models lagged significantly behind, with Random Forest reaching 89.65% accuracy and SVM only 67.62%. Overall, the results highlight the potential of combining UV-C fluorescence imaging with deep learning for rapid and reliable detection of E. coli on stainless steel and rubber conveyor belt surfaces. Additionally, this approach could support the design of effective interventions to remove E. coli from food processing environments. Full article

Salinization is a growing global problem, particularly in arid and semi-arid areas, where salt concentration interferes with the soil structure, altering natural cycling, decreasing agricultural outputs, and threatening food security. Although many soil amendments have been studied, there is still a limited understanding of their interaction with soil after mixture application and the geochemical processes and long-term sustainability that govern their effects. To address this knowledge gap, this review elucidated the effectiveness and sustainability of soil amendments, biochar, humic substances, and mineral additives in restoring saline and sodic soils of arid and semi-arid region to explore the geochemical processes that underlie their impact. A systematic search of 174 peer-reviewed studies was conducted across multiple databases (Web of Science, Google Scholar, and Scopus) using relevant keywords and the findings were converted into quantitative values to evaluate the effects of biochar, gypsum, zeolite, and humic substances on key soil properties. Biochar significantly improved cation exchange capacity, nutrient retention, microbial activity, and water retention by enhancing soil porosity and capillarity, thereby increasing plant-available water. Gypsum improved phosphorus availability, while zeolite facilitated the removal of sodium and supported microbial activity. Humic substances enhanced soil porosity, water retention, and aggregate stability. When applied together, these amendments improved soil health by regulating salinity, enhancing nutrient cycling, while also stabilizing soil conditions and ensuring long-term sustainability through improved geochemical balance and reduced environmental impacts. The findings highlight the critical role of multi-functional amendments in promoting climate-resilient agriculture and long-term soil health restoration in saline-degraded regions. Further research and field implementation are crucial to optimize their effectiveness and ensure sustainable soil management across diverse agricultural environments. Full article

Diabetes is a developing global health concern that cannot be cured, necessitating frequent blood glucose monitoring and dietary management. Photoacoustic Spectroscopy (PAS) in the mid-infrared (MIR) region has recently emerged as a viable noninvasive blood glucose monitoring technique. However, MIR-PAS confronts significant challenges: (i) Water absorption, which reduces light penetration, and (ii) interference from other blood components. This paper systematically analyzes the background of photoacoustic signal generation and proposes a differential PAS (DPAS) in the MIR region for removing the background signals arising from water and other interfering components of blood, which improves the overall detection sensitivity. A detailed mathematical model with an explanation for choosing two suitable MIR quantum cascade lasers for this differential scheme is presented here. For single-wavelength PAS (SPAS), a detection sensitivity of $1.537$ µPa mg⁻¹ dL was obtained from the proposed model. Alternatively, $2.333$µPa mg⁻¹ dL detection sensitivity was found by implementing the DPAS scheme, which is about 1.5 times higher than SPAS. Moreover, DPAS facilitates an additional parameter, a differential phase shift between two laser responses, that has an effective correlation with the glucose concentration variation. Thus, MIR-based DPAS could be an effective way of monitoring blood glucose levels noninvasively in the near future. Full article

Accurate processing of low-probability-of-intercept (LPI) radar signals poses a critical challenge in electronic warfare support (ES). These signals are often transmitted at very low signal-to-noise ratios (SNRs), making reliable analysis difficult. Noise interference can lead to misinterpretation, potentially resulting in strategic errors and jeopardizing the safety of friendly forces. Accordingly, effective noise suppression techniques that preserve the original waveform shape are crucial for reliable analysis and accurate parameter estimation. In this study, we propose the recognize-then-denoise network (RTDNet), which effectively removes noise while minimizing signal distortion. The proposed approach first employs a modulation recognition network to infer the modulation scheme and then feeds the inferred label to an attention-based denoiser to guide feature extraction. By leveraging prior information, the attention mechanism preserves key features and reconstructs challenging patterns such as polytime and polyphase codes. Simulation results indicate that RTDNet more effectively removes noise while maintaining the waveform shape and salient signal structures compared with existing techniques. Furthermore, RTDNet improves modulation classification accuracy and parameter estimation performance. Finally, its compact model size and fast inference meet the performance and efficiency requirements of ES missions. Full article

This study investigates the activation mechanism of boron-doped carbon (BMC) catalysts for the degradation of the antibiotic sulfamethoxazole (SMX) via persulfate (PMS) activation. The catalysts were synthesized using a sequential double-melting calcination method, resulting in mesoporous carbon nanosheets characterized by hierarchical macro-mesopores and atomically dispersed dual active sites. Comprehensive characterization was performed using BET, SEM, TEM, FT-IR, XPS, XRD, and Raman techniques. The optimized BMC catalyst demonstrated excellent performance, achieving complete removal of sulfamethoxazole (100%) and a high mineralization rate (~90%) within 45 min. Mechanistic analysis, including electron paramagnetic resonance (EPR), revealed that the degradation predominantly follows a singlet oxygen (1O2)-dominated pathway. The system exhibited broad applicability to various pollutants, along with notable operational stability and robust resistance to common environmental interferents. Persulfate activation was primarily attributed to boron-active sites, while the hierarchical mesoporous structure facilitated both pollutant enrichment and catalytic efficiency. Full article