Search Results (10,066)

Background and Clinical Significance: Psoriasis, a chronic immune-mediated inflammatory disease, can affect musculoskeletal structures, including the Achilles tendon. Achilles pain in psoriasis patients may arise from tendinitis or neuropathic pain due to peripheral nerve dysfunction, such as sural nerve (SN) involvement, a condition frequently misdiagnosed due to limitations in conventional diagnostics. Fascial tissues are critical in nerve compression syndromes. This case explores the application of a novel quantitative Sonoguide Digital Palpation (SDP) protocol and ultrasound (US)-guided hydrodissection (HD) for SN dysfunction mimicking Achilles tendinopathy in a psoriasis patient. Case Presentation: A 41-year-old male with psoriasis presented with acute onset of right heel stiffness and paresthesia. Physical examination, radiographs, and ultrasound were performed. SDP, employing a validated four-criterion diagnostic framework (including fascial mobility quantification and concordant pain provocation), identified crural fascia restriction affecting SN and reproduced patient’s concordant Achilles pain. High-resolution ultrasonography provided key morphological evidence, revealing a 2.6-fold enlargement of the sural nerve’s cross-sectional area (CSA) on the affected side (13 mm²) compared to the asymptomatic side (5 mm²). Notably, a positive Tinel’s sign was elicited over the psoriatic plaque. US-guided HD was performed using 50 cc of 5% dextrose in water (D5W) without local anesthetic below the psoriatic lesion. Post-HD, the patient reported immediate and significant pain relief (Numeric Pain Rating Scale (NPRS) score reduction from 8 to 2), confirming the prompt correction of a clinically important fascial restriction, associated with improved SN mobility, objectively verified by a post-procedure SDP assessment. At 24-month follow-up, sustained symptom relief and complete functional recovery were reported. Conclusions: This case highlights SDP’s ability to objectively visualize and confirm fascial restriction as a cause of nerve dysfunction by quantitatively reproducing concordant pain. The objective finding of nerve swelling provides sonographic substantiation for the functional diagnosis of nerve dysfunction. This integrated diagnostic approach, combining dynamic functional assessment with morphological confirmation, offers a novel paradigm for evaluating peripheral nerve disorders. US-guided HD of the SN with D5W without local anesthetic shows promise as both a diagnostic confirmatory tool and therapeutic intervention for neuropathic Achilles pain in psoriasis patients with SN involvement, aligning with its efficacy in other peripheral neuropathies. The significant nerve swelling (13 mm²) provides robust morphological corroboration of the functional impairment diagnosed by SDP, offering a more comprehensive diagnostic paradigm. Full article

With the increasing demand for precision in seismic exploration, high-resolution surveys and shallow-layer identification have become essential. This requires higher sampling frequencies during seismic data acquisition, which shortens seismic wavelengths and enables the capture of high-frequency signals to reveal finer subsurface structural details. However, the insufficient sampling rate of existing petroleum instruments prevents the effective capture of such high-frequency signals. To address this limitation, we employ high-frequency geophones together with high-density and high-frequency acquisition systems to collect the required data. Meanwhile, conventional processing methods such as Fourier transform-based time–frequency analysis are prone to phase instability caused by frequency interval selection. This instability hinders the accurate representation of subsurface structures and reduces the precision of shallow-layer phase identification. To overcome these challenges, this paper proposes a denoising method for high-sampling-rate seismic data based on Variational Mode Decomposition (VMD) optimized by the Starfish Optimization Algorithm (SFOA). The denoising results of simulated signals demonstrate that the proposed method effectively preserves the stability of noise-free regions while maintaining the integrity of peak signals. It significantly improves the signal-to-noise ratio (SNR) and normalized cross-correlation coefficient (NCC) while reducing the root mean square error (RMSE) and relative root mean square error (RRMSE). After denoising the surface mountain drilling-while-drilling signals, the resulting waveforms show a strong correspondence with the low-velocity zone interfaces, enabling clear differentiation of shallow stratigraphic distributions. Full article

Accurate segmentation of crops and weeds is essential for enhancing crop yield, optimizing herbicide usage, and mitigating environmental impacts. Traditional weed management practices, such as manual weeding or broad-spectrum herbicide application, are labor-intensive, environmentally harmful, and economically inefficient. In response, this study introduces a novel precision agriculture framework integrating Unmanned Aerial Vehicle (UAV)-based remote sensing with advanced deep learning techniques, combining Super-Resolution Reconstruction (SRR) and semantic segmentation. This study is the first to integrate UAV-based SRR and semantic segmentation for tobacco fields, systematically evaluate recent Transformer and Mamba-based models alongside traditional CNNs, and release an annotated dataset that not only ensures reproducibility but also provides a resource for the research community to develop and benchmark future models. Initially, SRR enhanced the resolution of low-quality UAV imagery, significantly improving detailed feature extraction. Subsequently, to identify the optimal segmentation model for the proposed framework, semantic segmentation models incorporating CNN, Transformer, and Mamba architectures were used to differentiate crops from weeds. Among evaluated SRR methods, RCAN achieved the optimal reconstruction performance, reaching a Peak Signal-to-Noise Ratio (PSNR) of 24.98 dB and a Structural Similarity Index (SSIM) of 69.48%. In semantic segmentation, the ensemble model integrating Transformer (DPT with DINOv2) and Mamba-based architectures achieved the highest mean Intersection over Union (mIoU) of 90.75%, demonstrating superior robustness across diverse field conditions. Additionally, comprehensive experiments quantified the impact of magnification factors, Gaussian blur, and Gaussian noise, identifying an optimal magnification factor of 4×, proving that the method was robust to common environmental disturbances at optimal parameters. Overall, this research established an efficient, precise framework for crop cultivation management, offering valuable insights for precision agriculture and sustainable farming practices. Full article

La³⁺/Bi³⁺ co-doped BaTiO₃ ceramics were synthesized via ball milling followed by heat treatment at 1200 °C according to the Ba_1−3xLa_2xTi_1−3xBi_4xO₃ formula, with dopant levels ranging from x = 0.0 to 0.006. X-ray diffraction and Rietveld refinement confirmed a ferroelectric tetragonal phase for all compositions, with the highest tetragonality (c/a = 1.009) observed for x = 0.001 exceeding that of pure BaTiO₃ (1.0083). High-resolution electron microscopy analysis revealed faceted particles with mean sizes between 362.5 nm and 488.3 nm. Low-doped samples (x = 0.001 and 0.002) exhibited higher permittivity than undoped BaTiO₃, with the maximum dielectric constant (ε_r = 2469.0 at room temperature and 7499.7 at the Curie temperature) recorded for x = 0.001 at 1 kHz. At x = 0.006, minimal permittivity variation indicated a stable dielectric response. A decrease in the Curie temperature was observed with increasing doping levels, indicating a progressive tendency toward the cubic phase. Critical exponent γ values (0.94–1.56) indicated a sharp phase transition for low-doped samples and a diffuse transition for highly doped BaTiO₃. These results showed that La³⁺/Bi³⁺ co-doping effectively tunes the structural and dielectric properties of BaTiO₃ ceramics. Full article

We present a stretched radial trajectory design that enhances image resolution in MRI by expanding k-space coverage without increasing readout duration or scan time. The method dynamically modulates gradient amplitudes as a function of projection angle, achieving square k-space coverage in 2D and cubic coverage in 3D imaging. Validation was conducted using phantom and in vivo experiments on GE and Siemens scanners at 0.55 T and 3 T. Point spread function analysis and reconstructed images demonstrated improved sharpness and clearer visualization of fine structures, including small phantom details and brain vasculature. The approach also increased T₁ and T₂ mapping accuracy in MRF acquisitions. The proposed strategy requires no additional scan time or gradient hardware capability, making it well-suited for MRI systems with moderate performance. It offers a simple and generalizable means to improve spatial resolution in both structural and quantitative imaging applications. Full article

Habitat complexity plays a critical role in coral reef ecosystems by enhancing habitat availability, increasing ecological resilience, and offering coastal protection. Structure-from-motion (SfM) photogrammetry has become a standard approach for quantifying habitat complexity in reef monitoring programs. However, a major bottleneck remains in the two-dimensional (2D) classification of benthic cover in three-dimensional (3D) models, where experts are required to manually annotate individual colonies and identify coral species or taxonomic groups. With recent advances in deep learning and computer vision, automated classification of benthic habitats is possible. While some semi-automated tools exist, they are often limited in scope or do not provide semantic segmentation. In this investigation, we trained a convolutional neural network with the ResNet101 architecture on three years (2015, 2017, and 2019) of human-annotated 2D orthomosaics from Kiritimati, Kiribati. Our model accuracy ranged from 71% to 95%, with an overall accuracy of 84% and a mean intersection of union of 0.82, despite highly imbalanced training data, and it demonstrated successful generalizability when applied to new, untrained 2023 plots. Successful automation depends on training data that captures local ecological variation. As coral monitoring efforts move toward standardized workflows, locally developed models will be key to achieving fully automated, high-resolution classification of benthic communities across diverse reef environments. Full article

This work proposes a photonics-aided W-band integrated sensing and communication (ISAC) system using photonics-aided frequency multiplication to suppress phase noise. Conventional dual-laser architectures suffer from phase noise accumulation, degrading both communication reliability and sensing resolution. To address this, we integrate photonics-aided frequency multiplication with orthogonal frequency-division multiplexing (OFDM), enabling a unified signal structure that simultaneously encodes communication data and radar waveforms without redundant resource allocation. Theoretical analysis reveals phase noise cancellation through coherent beating of symmetrically filtered sidebands in the photodetector (PD). Results demonstrate concurrent delivery of probability shaping (PS)-256QAM OFDM signals with a symbol error rate below 4.2 × 10⁻² and radar sensing with a 13.6 dB peak-to-sidelobe ratio (PSLR). Under a 1 MHz laser linewidth, the system achieves a 3.2 dB PSLR improvement over conventional methods, validating its potential for high-performance ISAC in beyond-5G networks. Full article

This paper focuses on the vibration analysis of a prototype helicopter rotor test stand, with particular attention to the dynamic response of its electric propulsion system. The stand is driven by an induction motor and equipped with composite rotor blades of various geometries, including blades with shape memory alloy (SMA)-based torsion actuators for angle of attack (AoA) adjustment. These variable geometries significantly influence the system’s dynamic behavior, where resonance phenomena may pose risks to structural integrity. The objective was to investigate how selected operational parameters specifically motor speed and AoA affect the vibration response of the propulsion system. Structural vibrations were measured using a tri-axial piezoelectric accelerometer system integrated with calibrated signal conditioning and high-resolution data acquisition modules. This setup enabled precise, time-synchronized recording of dynamic responses along all three axes. Fast Fourier Transform (FFT) and Power Spectral Density (PSD) methods were applied to identify dominant frequency components, including those associated with rotor harmonics and SMA activation. The highest vibration amplitudes were observed at an AoA of 16°, but all results remained within the vibration limits defined by MIL-STD-810H for rotorcraft drive systems. The study confirms the importance of sensor-based diagnostics in evaluating electromechanical propulsion systems operating under dynamic loading conditions. Full article

Traditional time-frequency diagnostics for high-speed bearings face an entrenched trade-off between resolution and real-time feasibility. We present a fast Dual-Phase Short-Time Root-MUSIC pipeline that exploits Hankel structure via FFT-accelerated Lanczos bidiagonalization and Sliding-window Singular Value Decomposition to deliver sub-Hz super-resolution under millisecond budgets. Validated on the Politecnico di Torino aerospace dataset (seven fault classes, three severities), fDSTrM detects 150 μm inner-race and rolling-element defects with 98% and 95% probability, respectively, at signal-to-noise ratio down to −3 dB (78% detection), while Short-Time Fourier Transform and Wavelet Packet Decomposition fail under identical settings. Against classical Root-MUSIC, the approach sustains approximately 200 times speedup with less than ${10}^{-11}$ relative frequency error in offline scaling, and achieves 1.85 milliseconds per 4096-sample frame on embedded-class hardware in streaming tests. Subspace order pre-estimation with adaptive correction preserves closely spaced components; Kalman tracking formalizes uncertainty and yields 95% confidence bands. The resulting early warning margin extends maintenance lead-time by 24–72 h under industrial interferences (Gaussian, impulsive, and Variable Frequency Drive harmonics), enabling field-deployable super-resolution previously constrained to offline analysis. Full article

Cell migration through confined spaces is a critical step in cancer metastasis, yet the spatial regulation of endocytosis and adhesion dynamics during this process remains poorly understood. To investigate this, we adapted a microfluidic platform that generates stable, spatially linear biochemical gradients across 5 μm-tall migration channels. COMSOL simulations and optical calibration using FITC-dextran confirmed that gradients form reliably within 5 min. The microdevice also supports long-term live imaging and is compatible with both spinning disk confocal and total internal reflection fluorescence structured illumination microscopy modalities, enabling high-resolution visualization of adhesion and endocytic structures. By leveraging this platform for spatially restricted drug delivery, we locally applied the endocytic inhibitor Dyngo-4a to either the front or rear of migrating cells. This revealed that front-targeted endocytic inhibition preserved or increased leading-edge enrichment of paxillin and the clathrin adaptor AP-2, whereas rear-targeted inhibition eliminated paxillin polarity and reduced AP-2 polarity. These changes were accompanied by a significant increase in cell migration speed under front-targeted inhibition, while rear-targeted inhibition had no significant effect on speed and neither treatment altered persistence. Together, these findings suggest that endocytic polarity regulates adhesion dynamics and cell migration under confinement, offering a mechanistic insight into processes relevant to cancer cell invasion. Full article

As carbon cycling and global environmental protection gain increasing attention, forest disturbance research has intensified worldwide. Constrained by limited data availability, existing frameworks often rely on extracting individual spectral bands for simple binary disturbance detection, lacking systematic approaches to visualize and classify causes of disturbance over large areas. Accurately identifying disturbance types is critical because different disturbances (e.g., fires, logging, pests) exhibit vastly different impacts on forest structure, successional pathways and, consequently, forest carbon sequestration and storage capacities. This study proposes an integrated remote sensing and deep learning (DL) method for forest disturbance type identification, enabling high-precision monitoring in Northeast China from 1992 to 2023. Leveraging the Google Earth Engine platform, we integrated Landsat time-series data (30 m resolution), Global Forest Change data, and other multi-source datasets. We extracted four key vegetation indices (NDVI, EVI, NBR, NDMI) to construct long-term forest disturbance feature series. A comparative analysis showed that the proposed convolutional neural network (CNN) model with six feature bands achieved 5.16% higher overall accuracy and a 6.92% higher Kappa coefficient than a random forest (RF) algorithm. Remarkably, even with only six features, the CNN model outperformed the RF model trained on fifteen features, achieving a 0.4% higher overall accuracy and a 0.58% higher Kappa coefficient, while utilizing 60% fewer parameters. The CNN model accurately classified forest disturbances—including fires, pests, logging, and geological disasters—achieving a 92.26% overall accuracy and an 89.04% Kappa coefficient. This surpasses the 81.4% accuracy of the Global Forest Change product. The method significantly improves the spatiotemporal accuracy of regional-scale forest monitoring, offering a robust framework for tracking ecosystem dynamics. Full article

The development of serial crystallography (SX), including serial synchrotron crystallography (SSX) at synchrotron sources and serial femtosecond crystallography (SFX) at X-ray free-electron lasers (XFELs), has facilitated the collection of high-resolution diffraction data from micron-sized crystals, providing unique insights into the structures and dynamics of biomolecules at room temperature. Standard samples are essential for the commissioning of new XFEL instruments and the validation of experimental setups. In this review, we summarize currently used standard proteins and describe representative microcrystal preparation workflows for four widely adopted models, lysozyme, myoglobin, iq-mEmerald, and photoactive yellow protein (PYP), drawing on established methodologies and accumulated experience from their applications at the European XFEL. By consolidating existing knowledge and integrating protocols that have been systematically refined and optimized through our experimental efforts, this review aims to provide practical guidance for the serial crystallography community, thereby enhancing reproducibility and ensuring consistent experimental performance across facilities. Full article

The paper presents comprehensive and complementary studies of the thermophysical properties of functional composite structures. The term functional in this case means the study of the structure while maintaining its post-production imperfections, as opposed to the study of material samples prepared solely for this purpose. The paper presents the results of experimental studies, followed by an analysis of thermophysical properties characterizing heat accumulation and anisotropic heat transfer of two types of utility composites. Composites with an epoxy matrix and two types of reinforcement, glass and carbon fibers, were studied. The research program included micro- and macrostructural analysis and comprehensive thermogravimetric, microcalorimetric and thermal diffusivity measurements. In the studies of heat transfer phenomena, the directional dependence of properties was considered. Attention was focused on maintaining high temperature resolution of measurements, and the effect of repeated temperature exposure was also determined. The results of the research are the determined quantitative and qualitative characteristics, including the temperature dependence of a set of thermophysical properties of the tested materials. Key findings include higher thermal stability and a significant thermal anisotropy ratio in the graphite-reinforced polymer composite compared to the glass-reinforced polymer composite, which exhibited a lower onset decomposition temperature. The results offer crucial data for engineering calculations, structural analyses, and defining operational limits. Analysis of the results provides insight into possible design and operational problems of real structures in relation to model data. Full article

Exploration for hydrocarbon reservoirs is currently focused on increasingly difficult targets and geological structures, thus stimulating a growing requirement for new measurement methods and techniques that can provide more detailed information about lithology and reservoir parameter distribution in the vicinity of the target zone. This publication presents a method for increasing the resolution of the recorded surface seismic wavefield in the vicinity of example borehole Well-1 (western Poland) for reservoir horizons of the Rotliegend and Zechstein formations. The main stage of the research was the introduction of frequencies from vertical seismic profiling (VSP) into seismic traces. The shape filter deconvolution procedure was applied based on the operator calculated from VSP data, which was applied to seismic profiles extracted from 3D data. The procedure applied allowed for the reconstruction of higher-frequency spectrum necessary for a detailed imaging of the geological framework of the analyzed reservoir formations. In the next stage, seismic inversion calculations were conducted, both on VSP data (corridor stack and VSP-CDP transformation) and on surface seismic time sections. The results obtained as an acoustic impedance distribution enabled a more comprehensive structural interpretation and detailed analysis of the variability of reservoir properties in the analyzed well area. Full article

Hurricanes are increasingly impacting inland water systems, yet their role in mobilizing anthropogenic contaminants remains insufficiently characterized. This study presents a preliminary assessment of organic contaminant loading in the French Broad River, North Carolina, 15 days after flooding from Hurricane Helene. Surface water samples from five sites were analyzed using liquid chromatography–high-resolution mass spectrometry. Targeted analysis quantified 11 per- and polyfluoroalkyl substances (PFASs), with summed concentrations ranging from 1.49 to 70.8 ng/L. One downstream site exhibited the highest burden, where PFOSs and PFOA exceeded U.S. EPA drinking water maximum contaminant levels. Non-targeted analysis (NTA) and suspect screening identified 468 compounds, with 96 structurally annotated at high confidence. Of these, a large proportion were associated with medium to high hazard potential, particularly for acute aquatic toxicity (69%), developmental toxicity (64%), mutagenic genotoxicity (49%), endocrine disruption (35%), skin irritation (27%), eye irritation (26%), and carcinogenicity (17%). Four EPA priority pollutants—4-nitrophenol, 2,4,6-trichlorophenol, pentachlorophenol, and dibutyl phthalate—were also detected. Site-specific chemical profiles suggested inputs from flood-damaged wastewater infrastructure and plastic debris. These findings highlight the susceptibility of freshwater systems to contaminant mobilization during extreme flooding and demonstrate the value of combining NTA and cheminformatics for post-disaster monitoring. Full article