Search Results (2,158)

Cotton, a globally significant crop grown in over 100 countries, sustains a $40 billion market and provides employment for over 350 million people worldwide. However, plastic contamination remains a persistent challenge within the industry, degrading cotton fiber quality and disrupting ginning. Manual inspection and optical machine-vision systems struggle when plastic fragments are concealed by fibers or lack sufficient color contrast. To address these challenges, we developed an ultrasonic phased-array imaging system operating at 40 kHz under field-programmable gate array (FPGA) control. Transmitter elements emit pulsed ultrasound along radial paths, separate reflection receivers record echo amplitudes to form acoustic images, and a set of transmission receivers captures signal attenuation, which is overlaid onto the reflection-based image to highlight potential contaminants. In preliminary laboratory-based tests on both seed cotton and lint samples, the system successfully detected visually obscured plastic fragments as small as $2\mathrm{cm}\times 2\mathrm{cm}$ with an angular resolution limit of $\pm 3^{\circ }$. Distinct reflection peaks and corresponding attenuation overlays were produced across the field of view, validating the system’s detection capabilities. These results demonstrate the feasibility of using ultrasonic imaging to reveal concealed plastics in cotton processing. Integrating this approach with existing optical methods could enhance contaminant-removal workflows and improve overall fiber quality and processing efficiency. Full article

Event-based vision sensors (EVSs) provide unique frequency analysis opportunities due to their event data output and high temporal resolution. Anomaly detection methods used in hyperspectral analysis can be used on the event frequency spectra to detect targets. However, the introduction of a strong, flickering interfering source can reduce the EVS sensitivity and obscure targets of interest. Previous work presented a method showing that targets could still be detected through an overwhelming source using frequency analysis, background suppression, and statistical filtering. This paper extends that research and compares the ability of five different eigenanalysis anomaly detection methods (principal component background suppression (PCBS) with peak threshold detection, Mahalanobis distance (MD) detector, complementary subspace detector (CSD), Reed–Xiaoli (RX) detector, and subspace Reed–Xiaoli (SSRX) detector) to detect targets in a high noise environment. The PCBS, MD, and CSD detectors performed well and were able to detect the targets through the overwhelming source. The PCBS detector had the best performance at low false-alarm rates (a > 400% detection probability increase at a false-alarm probability of 10⁻⁵). While the MD and CSD detectors had the best detection at higher false-alarm probabilities (approximately 7 × 10⁻²), the MD detector had a sub-second execution time. Depending on the application, the PCBS or MD detector are the best choice out of these five methods to detect targets in this type of high noise environment. Full article

Traffic variability along heavily congested signalised urban corridors undermines roadway safety, reduces energy efficiency, weakens operational reliability, and can hinder emergency response. Although many simulation-based studies have examined the impacts of Autonomous Vehicles (AVs), relatively few have combined high-resolution congestion observations with link-level microscopic calibration in a real urban network, particularly when evaluating implications for emergency mobility. This study develops and calibrates a microscopic Aimsun traffic simulation model for the Atakum district of Samsun, Türkiye, using a 10 min Google Traffic Index (GTI) observation stream converted into a four-level ordinal congestion scale. The calibration process began with an origin–destination (OD) matrix derived from 2020 traffic counts and was refined through link-level GTI synchronization, iterative OD scaling on mismatched corridors, and signal retiming at key intersections. GTI was validated as an ordinal congestion proxy through both categorical agreement and volumetric consistency, achieving 83% class agreement and GEH values below 5 for more than 90% of links. Five AV penetration scenarios (0%, 25%, 50%, 75%, and 100%) were simulated under peak-hour conditions. Network performance was evaluated using delay, stop time, mean speed, throughput, missed turns, and total journey time, while emergency mobility was assessed along a representative ambulance corridor on Atatürk Boulevard using seconds per kilometre. The results indicate that increasing AV penetration improves flow stability more clearly than nominal capacity. Mean speed increased from 36.2 to 39.2 km/h, delay and stop time declined steadily, and throughput remained nearly constant at 22.2–22.5 thousand vehicles/h. Along the ambulance corridor, travel time improved by 11.5%, from 112.4 to 99.4 s/km, between the baseline and full automation scenarios. These findings provide scenario-based evidence that, within a calibrated signalised urban network, increasing AV penetration can enhance operational stability and emergency response efficiency. More broadly, the study demonstrates the practical value of integrating GTI-based congestion observations with microscopic simulation for AV impact assessment in real urban networks. Full article

Accurate monitoring of water spatiotemporal dynamics is critical for hydrological process analysis and climate impact assessment. While remote sensing enables effective water monitoring, public satellite imagery is limited by mixed-pixel effects that hinder small river detection, and high-resolution commercial data suffers from low temporal frequency and restricted coverage. To address these limitations, this study proposes a deep learning-based super-resolution (SR) framework for multispectral remote sensing imagery. This paper constructs a matched dataset for GF2 and Sentinel-2 imagery and develops an Attention Enhanced Super Resolution Generative Adversarial Network (AESRGAN). By integrating attention mechanisms and a spectral-structural loss design, the network is optimized to adapt to the characteristics of multispectral remote sensing imagery. Experimental results demonstrate that AESRGAN achieves strong reconstruction performance, with a Peak Signal-to-Noise Ratio (PSNR) of 33.83 dB and a Structural Similarity Index Measure (SSIM) of 0.882. Water extraction based on the reconstructed imagery using the U-Net++ model achieved an overall accuracy of 0.97 and a Kappa coefficient of 0.92. In addition, the reconstructed imagery improved the estimation accuracy of river length, width, and area by 0.34%, 3.28%, and 8.51%, respectively. The proposed framework provides an effective solution for multi-source remote sensing data fusion and high-precision surface water monitoring, offering new potential for long-term hydrological observation using medium-resolution satellite imagery. Full article

Carbon dioxide (CO₂) enrichment using fuel combustion is widely applied in greenhouse production. However, its implications for air quality and occupational safety under real operating conditions remain insufficiently characterized. This study evaluates a propane-based CO₂ enrichment system in an advanced greenhouse. The analysis integrates CO₂ dynamics, combustion-derived pollutants, and occupational exposure. High-resolution monitoring at 5 min intervals was conducted in an enriched module and a control module over a five-month period. Two operational modes were assessed: continuous and diurnal-only enrichment. The system maintained CO₂ concentrations within agronomic targets. Mean values reached 1200 ppm and 940 ppm for continuous and diurnal operation, respectively. However, significant CO₂ losses were observed due to ventilation. The maximum enrichment efficiency, expressed as the Combustion Efficiency Index (CEI), was 2.67 × 10⁻³. Combustion-related pollutants (CO, NO, NO₂, SO₂, and O₃) showed transient peaks during burner activation. However, concentrations remained below occupational exposure limits when evaluated using time-weighted averages. The incomplete combustion ratio (ICR) remained stable at approximately 1.9 × 10⁻³. This indicates predominantly complete combustion. These results provide field-based evidence on the performance and safety of propane-based CO₂ enrichment systems. They also highlight the importance of continuous monitoring and improved CO₂ retention strategies in semi-confined greenhouse environments. Full article

Monitoring dam stability is critical to ensure structural safety and operational reliability. This study integrates Persistent Scatterer Interferometry (PSI) based on Sentinel-1 SAR imagery (2020–2023) with Finite Element Method (FEM) simulations to assess the behavior of the Koyna Dam in India. PSI detected crest displacements between −1.0 and −1.8 mm yr⁻¹, while FEM simulations predicted a maximum vertical displacement of approximately −3.2 mm at the crest. Although these results represent different quantities (time-averaged displacement rates versus peak static displacement), both approaches indicate millimeter-scale deformation and a consistent pattern of settlement at the dam crest, supporting the interpretation of hydrologically driven structural response. The observed differences are primarily attributed to differences in spatial resolution and methodology between point-based FEM outputs and pixel-averaged satellite observations. The study demonstrates that combining satellite-based monitoring with numerical simulations provides a robust and cost-effective framework for dam safety assessment. This integrated approach supports improved interpretation of deformation behavior and offers practical value in extreme conditions, such as during flood events or climate-driven hydrological changes. Furthermore, continued advances in remote sensing and numerical modeling are expected to enhance the reliability of such approaches, making this methodology a transferable and sustainable solution for dam management worldwide. Full article

High-frequency endoscopic ultrasound is crucial for the early diagnosis of gastrointestinal tumors. However, achieving high axial resolution, deep tissue signal-to-noise ratio, and real-time data processing simultaneously remains a significant challenge in hardware implementation. This paper proposes a miniaturized real-time high-frequency imaging system based on the Xilinx Artix-7 FPGA. To overcome attenuation limitations of high-frequency signals, we employ a 4-bit Barker code-encoded excitation scheme coupled with a programmable ±100 V high-voltage transmission circuit. This effectively enhances echo energy without exceeding peak voltage safety thresholds. At the receiver end, the system utilizes a multi-channel analog front end integrated with mixed-signal time-gain compensation technology. Furthermore, to address transmission bottlenecks for massive echo data, we designed a Low-Voltage Differential Signaling (LVDS) interface logic based on dynamic phase calibration, ensuring stable, high-speed data transfer to the host computer via USB 3.0. Experimental results with a 20 MHz transducer demonstrate that the system achieves real-time B-mode imaging at 30 frames per second. Phantom testing revealed an axial resolution of 0.13 mm, enabling clear differentiation of 0.1 mm microstructures. Compared to conventional single-pulse excitation, coded excitation technology improved signal-to-noise ratio (SNR) by approximately 4.5 dB at a depth of 40 mm. These results validate the system’s capability for high-precision deep imaging suitable for clinical endoscopy applications, delivered in a compact, low-power form factor. Full article

The integration of vertical greenery systems (VGSs) into existing reinforced concrete (RC) buildings raises questions regarding interface kinematics and the permanent displacement of soil-retaining elements under seismic excitation. This study experimentally investigates the residual displacement of façade-mounted living walls and rooftop planter pods anchored to a deficient RC frame under shake table excitation. A 1:3 scale reinforced concrete frame was tested in two distinct phases: initially as a deficient, unretrofitted structure (Phase A), and subsequently as a retrofitted system integrated with vertical greenery elements (Phase B). High-precision terrestrial laser scanning (TLS) was employed before and after successive seismic excitation stages to generate dense three-dimensional point clouds. Cloud-to-cloud comparison techniques were used to quantify global structural displacement and local kinematic behavior of greenery components, while results were validated against conventional displacement sensors. The RC frame exhibited millimeter-scale permanent displacements consistent with draw-wire measurements. In contrast, planter pods demonstrated configuration-dependent behavior, including up to 8 cm translational sliding and rotational responses reaching 13° under repeated excitation, whereas living wall panels remained stable. Notably, a 95% reduction in point cloud density reproduced global deformation patterns with an RMSE of 3.03 mm and quantified peak displacements with only ~2% deviation from full-resolution results. The findings demonstrate the capability of TLS-based monitoring to detect differential kinematic behavior of integrated VGSs, while highlighting the variability in performance of friction-based rooftop anchorage utilizing different robust planter pod fixing systems. Full article

In recent years, image denoising has seen a shift from traditional non-local self-similarity methods like BM3D to deep-learning based approaches that use learnable convolutions and attention mechanisms. While pixel-level attention is effective at capturing long-range relationships similar to non-local self-similarity based methods, it incurs extremely high computational costs that scale quadratically with image resolution. As an alternative, channel-wise attention is resolution-independent and computationally efficient but may miss crucial spatial details. In this paper, an adjustable attention mechanism is introduced that bridges the gap between pixel and channel attentions. In the proposed model, average pooling and variable-size convolutions are added before attention calculation to adjust spatial resolution and, thus, allow dynamical adjustment of computational complexity. This adjustable attention is applied in a transformer-based U-Net architecture and achieves performance comparable to state-of-the-art methods in both real and Gaussian blind denoising tasks. To be more concrete, the proposed method achieves a Peak Signal-to-Noise Ratio of 39.65 dB and a Structural Similarity Index Measure of 0.913 on the Smartphone Image Denoising Dataset. Therefore, the proposed method demonstrates a balance between efficiency and denoising quality. Full article

Urban disaster management frequently relies on isolated single-hazard assessments and static census data. This conventional approach systematically obscures the highly dynamic, time-varying nature of population exposure to co-located environmental hazards. This study develops an observation-based, time-adaptive multi-hazard exposure prioritization framework to quantify these spatiotemporal variations. We integrate seismic amplification susceptibility, derived from shear-wave velocity estimates, and empirical pluvial flooding footprints with hourly dynamic living population data at a 250 m grid resolution in Seoul, South Korea. Results indicate that multi-hazard integration refines spatial prioritization, with 11% of high-priority areas diverging from single-hazard models, primarily driven by highly amplifiable alluvial deposits. Furthermore, dynamic living population data revealed clear diurnal exposure shifts. Business districts exhibited a daytime-to-nighttime exposure ratio of 3.35, whereas residential areas showed an inverse ratio of 0.69, demonstrating that identical physical conditions generate markedly different exposure patterns depending on the daily urban rhythm. Based on these temporal dynamics, we classified high-priority zones into Persistent (79.4%), Day-peak (10.3%), and Night-peak (10.3%) transition types. These findings suggest that urban exposure must be managed as a time-varying attribute rather than a static feature. The proposed classification supports targeted mitigation: structural improvements for Persistent areas, dynamic crowd management for Day-peak zones, and localized alerts for Night-peak zones. Driven by globally accessible mobile data, this framework provides a transferable foundation for exposure-informed urban resilience planning across diverse metropolitan environments. Full article

As a leading method for Novel View Synthesis (NVS), 3D Gaussian Splatting (3DGS) faces limitations. Fixed thresholds governing Gaussian scale and opacity lead to over-reconstruction or under-reconstruction, while the linear penalty used for handling outliers during optimization tends to introduce artifacts. Therefore, we propose Adaptive 3DGS featuring a dynamic deletion mechanism. Specifically, our method calculates coverage for each Gaussian based on its scale during removal. Gaussians with high coverage face stricter scale thresholds to reduce over-reconstruction, while those with lower coverage receive lenient thresholds to preserve details. Simultaneously, transparency-based contribution assessment is applied. Gaussians with low contribution meet stricter transparency thresholds to combat over-reconstruction, while high-contribution ones get lenient thresholds to mitigate under-reconstruction. During optimization, introducing Huber loss promotes quadratic growth for small errors, reducing smoothing to alleviate artifacts and better preserve details. Evaluation on standard datasets shows our method improves peak signal-to-noise ratio (PSNR) by 0.3 dB over 3DGS and 0.5 dB over MS-3DGS at 4× resolution, and it achieves a 0.1 dB gain over Mip-Splatting, confirming its effectiveness and robustness. Full article

Traditional Chinese prescriptions are characterized by complex chemical constituents and wide variations in constituent content, which pose a substantial challenge to their comprehensive characterization. As a classic traditional Chinese prescription known for its heat-clearing and detoxifying properties, Huangqintang Decoction (HQD) is composed of Scutellariae Radix, Paeoniae Radix Rubra, Glycyrrhizae Radix et Rhizoma, and Jujubae Fructus. In this study, we developed an off-line two-dimensional liquid chromatography that addressed the limitations of traditional analysis of unfractionated extracts, such as restricted peak capacity, which often obscured trace components. By coupling with ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS), this study successfully performed rapid identification or characterization of the complete chemical profile of HQD. Notably, beyond high-throughput identification, this approach leveraged characteristic fragment ions and reversed-phase chromatographic behaviors to differentiate some isomers of flavonoid glycosides and triterpenoid saponins, demonstrating its depth in structural identification. Flavonoid glycoside isomers were distinguished by diagnostic neutral losses, while flavanones and chalcones were characterized by retro-Diels–Alder (RDA) and β-rearrangement, respectively. Isomers of triterpenoid saponins were inferred from aglycone-specific pathways alongside RDA cleavages. Ultimately, a total of 192 compounds were identified, including 88 flavonoids, 80 triterpenoids, 7 monoterpene glycosides, 3 fatty acid amides, 3 phenylethanoid glycosides, 4 coumarins, 3 saccharides, 1 organic acid, and 3 others. This study demonstrated that the off-line two-dimensional liquid chromatography analysis strategy significantly enhanced chromatographic resolution and expanded the coverage of trace components. It presented an effective strategy for comprehensive compound identification in complex traditional Chinese medicine prescriptions. Full article

A thin-film surface plasmon resonance (SPR) sensor is presented using a prism-coupled Kretschmann configuration and an optimized multilayer architecture incorporating black phosphorus (BP) as an ultrathin overlayer. The response is modeled at 633 nm under TM polarization using the transfer-matrix method. Low-concentration sensing conditions in the 1–5 ng/mL range are represented through small effective-refractive-index perturbations of the aqueous sensing medium, providing a preliminary optical framework for evaluating refractive-index response in biosensing-related scenarios. The coupling prism, Au film thickness, and Si₃N₄ spacer thickness are optimized to control resonance depth, linewidth, and angular shift. The optimized CaF₂/Au/Si₃N₄/BP configuration exhibits systematic condition-dependent displacement of the SPR minimum and an evanescent-field distribution that remains strongly localized at the sensing interface while extending into the sensing medium, enabling refractive-index interrogation. High angular sensitivity is obtained at low levels, reaching 517.62°/RIU at 2 ng/mL and 482.82°/RIU at 1 ng/mL, with quality factors above 120 RIU⁻¹ in the same regime. Composite indicators (figure of merit and contrast signal factor) peak at intermediate levels, whereas resonance broadening at higher levels reduces the quality factor and increases the inferred limit of detection, evidencing a sensitivity–resolution trade-off. Benchmarking against reported SPR platforms indicates that BP-assisted interface engineering provides a competitive low-level operating window within a preliminary refractive-index-sensing framework that is relevant to future biosensor design. These results motivate further experimental validation, including BP stabilization, surface biofunctionalization, and practical implementation under liquid-phase sensing conditions. Full article

Understanding the spatiotemporal dynamics and underlying drivers of invasive species is crucial for moving beyond descriptive monitoring to predictive management. The red imported fire ant (Solenopsis invicta Buren, RIFA) continues to spread globally, yet studies often lack the seasonal and cross-habitat resolution needed to explain the puzzling heterogeneity of infestations within urban landscapes—such as the stark contrast between high-density agricultural zones and low-density urban green spaces. To address this gap, we conducted a four-season, city-wide survey of 129 sites across four dominant habitat types (farmlands, fishponds, orchards, and urban green spaces) in Guangzhou, a core city of the GBA. Using inverse distance weighting interpolation, kernel density estimation, and spatial autocorrelation, we sought to examine not only the spatial patterns of RIFA distribution but also its potential contributing factors. Our analysis points to three key observations. First, the occurrence level of RIFA appears to follow a significant gradient (farmlands > fishponds > orchards > urban green spaces), suggesting that idle agricultural lands may serve as core reservoirs. Second, we observed a pronounced seasonal bimodal pattern, with peak infestation indices in spring and autumn—a dynamic that seems closely associated with agricultural disturbance cycles. Third, spatial analysis (Global Moran’s I = 0.346, p < 0.001) revealed significant clustering, with “high-high” clusters concentrated in peripheral suburban districts. Notably, abandoned or idle farmlands emerged as a potentially important factor, possibly acting as dispersal hubs that help bridge these spatial and temporal peaks and offering one explanation for how local outbreaks may spread across the landscape. Collectively, these findings suggest that RIFA distribution may not be driven solely by static habitat suitability or climate; instead, they point to the importance of considering the dynamic interplay between land-use legacies (such as abandonment), seasonal agricultural practices, and spatial connectivity. By elucidating these drivers, this study refines the theoretical framework of urban invasion biology and provides a replicable, evidence-based control paradigm. We suggest implementing a “zoned, seasonal, and pathway-specific” management strategy that prioritizes suburban farmland complexes during critical seasons and targets abandoned lands for intervention, offering a path towards more sustainable and precise regional RIFA control in the GBA and beyond. Full article

Lightweight multi-rotor unmanned aerial vehicles (UAVs) have shown great potential in flexible Earth observation, but they impose strict restrictions on payload, volume, and power consumption. Traditional pulse-Doppler synthetic aperture radar (SAR) systems offer high imaging performance but suffer from high peak power and large volume, making them unsuitable for lightweight UAV platforms. To meet the low-power demand, most existing lightweight UAV SAR systems adopt frequency-modulated continuous-wave (FMCW) schemes, which are compact and low cost yet limited by a low range resolution, poor anti-interference ability, and single imaging modes. Therefore, it is urgent to develop an SAR system that combines the high performance of pulse radar with the lightweight advantage of FMCW radar. To this end, this paper proposes a compact, low-power Ka-band pulse-Doppler SAR system for multi-rotor UAVs. With 1.2 GHz bandwidth and highly integrated RF and antenna design, the system achieves miniaturization and low power consumption while maintaining high-resolution imaging capability. Furthermore, two-step waveform error correction and a signal predistortion method are presented to compensate amplitude and phase errors and improve the purity of the transmitted signal. Experimental results show that the proposed system can obtain clear SAR images with a resolution better than 0.3 m, providing a practical high-performance pulse-SAR solution for lightweight UAV platforms. Full article