Search Results (226)

Traditional wood anatomy provides the gold standard for timber identification, yet its reliance on centralized laboratory infrastructure severely limits its efficacy during real-time field inspections. This study addresses a critical research question: How can physical xylotheque resources, national timber extraction registries, and edge-computing computer vision be integrated into a cohesive framework to enable robust, forensic-level wood identification at field control stations? To resolve this, we implemented a three-tier methodology: first, we audited historical records from Ecuador’s Forest Administration System (SAF) encompassing 129 commercial timber species; second, we conducted a gap analysis using the Wood Anatomy Laboratory and Xylotheque (LAMX) repository (510 cataloged samples, 2267 histological preparations) to secure botanically validated references; and third, we leveraged a curated database of high-resolution digital cross-section captures (4900 images) to evaluate CNN architectures via k-fold cross-validation and a standard 70/15/15% training/validation/testing split. Benchmarking demonstrated that the lightweight MobileNetV2 architecture achieved a global accuracy of 94.04% and an F1-score of 0.976. External field validation conducted across commercial timber yards in Ibarra confirmed an offline inference latency of just 145 ms on mid-range Android devices, proving the framework’s operational transparency and low-cost scalability. Furthermore, Explainable AI analysis using Class Activation Maps (Grad-CAM) provided visual evidence indicating that the neural network targeted diagnostic xylotomic features (vessel distribution and axial parenchyma), minimizing reliance on external environmental noise. In conclusion, this study demonstrates that hybridizing physical taxonomic reference collections with targeted edge AI models provides a scalable, transparent, and low-cost solution that successfully bridges academic research and active forest law enforcement in tropical regions. Full article

The internal flow of a semi-open axial flow fan is highly three-dimensional and unsteady due to the absence of a confined passage. The evolution of complex vortical structures, such as the tip leakage vortex (TLV) and corner separation vortex (CSV), remains poorly understood. This study used high-resolution particle image velocimetry (PIV) to conduct multi-region, multi-view measurements of the flow field in a semi-open fan for an outdoor air conditioning unit. The generation, development, and breakdown of the TLV were analyzed, revealing transient nonuniform flow and wake evolution. Dynamic mode decomposition (DMD) was applied to extract dominant frequencies and spatial modes. The results show that the TLV has a dominant frequency of 98.5 Hz (2.19 times the rotational frequency), accounting for 88.5% of the total energy, and exhibits periodic shedding and asymmetric breakdown. The CSV dominates at 16.44 Hz, slightly above blade rotation, and interacts with the TLV. In the wake region, the dominant frequency is 248.45 Hz, arising from the nonlinear superposition of TLV harmonics, the CSV frequency, and the blade passing frequency. This study provides an experimental basis and a low-dimensional coherent structure model for internal flow diagnostics and the structural optimization of semi-open axial flow fans. Full article

Optical coherence tomography (OCT) is an optical imaging modality that provides high-resolution cross-sectional imaging of biological tissues noninvasively. In Fourier-domain OCT, axial resolution is governed by both the center wavelength and the spectral bandwidth of the light source; therefore, limited or discontinuous bandwidth degrades depth resolution and introduces sidelobes and artifacts in OCT images. To address these issues in OCT image reconstruction from gapped spectra, a sparse parameter estimation approach based on Sparse Iterative Covariance-based Estimation (SPICE) is proposed in this study. By utilizing a sparse parameter estimation framework to directly resolve depth-dependent components from discontinuous interferograms, SPICE enhances axial resolution while suppressing sidelobe artifacts inherent in standard interpolation. Experiments on multi-layered tape, oral epithelium, and finger skin show that SPICE visually suppresses gap-induced sidelobe artifacts and improves structural interpretability under representative gap conditions. Quantitative evaluations on multi-layer tape and biological tissues show that SPICE reduces axial FWHM by 30–45%, increases SSIM by 0.15–0.25, and achieves significantly lower computational cost than GAPES (p < 0.01). Full article

This study presents experimental results from high-speed interferometric measurements on a transonic compressor blade cascade, where three of the five blades were torsionally oscillated at various frequencies up to $150\mathrm{Hz}$ and different inter-blade phase angles. The primary research objective is to develop and validate a non-intrusive methodology capable of quantifying unsteady flow fields surrounding aeroelastically unstable components. The resulting flow field images demonstrate the potential of the method. Unlike classical interferometric methods, the proposed approach has less stringent requirements for the optical quality of the test section windows. This advantage allows for the use of organic-glass windows, which are necessary for investigating highly loaded compressor blade cascades. Such windows are required to accommodate the suction slots used to maintain a representative Axial Velocity Density Ratio (AVDR). Unlike the classical schlieren technique, the method provides quantitative results with high spatial and temporal resolution, while the synthetic schlieren images can also be produced. The method proved suitable for measurements in the harsh environment of transonic flow through oscillating blades and is capable of capturing important unsteady flow phenomena. Full article

Continuous and objective monitoring of livestock behavior plays a key role in precision farming, animal welfare assessment, and reproductive management. This study proposes a non-invasive framework for sheep behavior and reproductive activity monitoring that integrates wearable actigraphy, machine learning, and a cloud-based data processing architecture. Tri-axial accelerometer data were collected at 30 Hz using collar-mounted ActiGraph sensors under real farming conditions. Raw acceleration signals were processed without temporal aggregation, preserving full temporal resolution that includes axis-specific acceleration, vector magnitude, and delta magnitude features. Several supervised learning models were evaluated for behavior classification, including BLSTM, LSTM, CNN–BLSTM, Random Forest, and Support Vector Machine, targeting behaviors such as standing, walking, grazing, lying, flehmen, and mating. The results indicate that both deep learning and classical machine learning approaches achieve high classification performance, with Random Forest obtaining an overall accuracy of 0.82, while deep sequential models effectively capture temporal patterns and behavioral transitions. Furthermore, a scalable cloud architecture is introduced to automate data ingestion, preprocessing, inference, storage in InfluxDB, and visualization through an interactive web application. The proposed framework supports continuous monitoring and offers practical tools for precision livestock management. Full article

Objective: In this study, we aimed to evaluate high-resolution computed tomography (HRCT) features of granulomatous–lymphocytic interstitial lung disease (GLILD) in patients with Common Variable Immunodeficiency (CVID), and to describe a novel imaging feature—termed the “Kebab sign”—as a potential radiologic marker of GLILD. Materials and Methods: We retrospectively reviewed HRCT scans of 15 patients with GLILD diagnosed between 2005 and 2025 at a single institution (seven biopsy-confirmed, eight probable diagnoses based on multidisciplinary consensus). CT patterns were assessed for predominant morphology (nodular, reticular, alveolar, fibrotic), distribution (axial and cranio-caudal), and presence of extra-parenchymal findings. Nodules were characterized by size, density, morphology, and the presence of air bronchograms. The “Kebab sign” was defined as nodules aligned along bronchial structures with associated peribronchial thickening. Results: All patients demonstrated a diffuse nodular pattern, with non-calcified macronodules in 100% and micronodules in 60% of cases. Air bronchograms were present in 87% of macronodules. A peri-bronchovascular distribution with lower lung predominance was observed in the majority of cases. The “Kebab sign” was identified in 87% of patients. Splenomegaly and hilar/mediastinal lymphadenopathy were observed in 75%. In 20% of patients, fibrosing features were also present, particularly in older individuals. Conclusions: HRCT findings of GLILD typically include peri-bronchovascular nodules with lower lobe predominance, typically associated with splenomegaly and mediastinal lymphadenopathy. The newly described “Kebab sign,” reflecting nodular alignment along thickened bronchial structures, may represent a useful imaging clue to support the diagnosis of GLILD. Full article

Background: This study aimed to evaluate the image quality of non-contrast-enhanced whole-heart coronary MR angiography (CMRA) using three different sequences: coronal-plane balanced turbo field echo (BTFE) at 3T, axial-plane modified Dixon (mDixon) at 3T, and axial-plane mDixon at 5T. Methods: Healthy young volunteers were prospectively enrolled from January 2025 to April 2025. Each participant underwent three CMRA scans—3T BTFE, 3T mDixon, and 5T mDixon—using customized MR protocols, all performed within 48 h. Subjective image quality was assessed based on the society of cardiovascular computed tomography 18-segment model using a four-point scale (1 = non-assessable to 4 = excellent). The assessability rate was defined as the percentage of segments receiving a score ≥ 2. Objective evaluation of the main coronary arteries included measurements of signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), vessel edge sharpness (VES), and visible vessel length. The Friedman test and one-way repeated measures analysis of variance (ANOVA) were performed to compare parameters obtained from 3T BTFE, 3T mDixon, and 5T mDixon. Results: A total of 20 participants (10 men; mean age, 24 ± 2 years) were included. Both 5T mDixon and 3T BTFE showed more favorable subjective image quality than 3T mDixon, particularly in distal and branch-level coronary segments. All three sequences achieved high vessel assessability. Quantitatively, 5T mDixon provided the highest SNR and CNR, while 3T BTFE showed the highest VES. Visible vessel lengths in LAD and RCA were longer with 5T mDixon and 3T BTFE versus 3T mDixon. However, 5T mDixon required the longest acquisition time (12.55 ± 2.80 min), consistent with its higher spatial resolution. Conclusions: In conclusion, in healthy volunteers, both 5T mDixon and 3T BTFE outperformed 3T mDixon in non-contrast CMRA, particularly in distal and branch-level coronary segments. While 5T mDixon provided the highest SNR and CNR, 3T BTFE achieved the greatest VES. These findings support the technical feasibility of both approaches, but further studies in patients are needed to confirm their clinical applicability Full article

Airblast sprayers remain the dominant pesticide delivery system in California citrus; however, mechanistic characterization of spray transport and off-target fate under realistic field-scale atmospheric variability remains limited. Regulatory airblast drift assessments in the United States (U.S.) currently rely on a sparse, dormant-apple canopy representation, despite substantial structural differences from foliated citrus canopies that may influence drift behavior. To address this gap, this study quantified airblast spray drift in a commercial citrus orchard across multiple downwind distances under varied daytime meteorological conditions and evaluated the influence of distance and weather variables on measured drift. Airborne and sedimentation drift were measured from a conventional axial-fan airblast sprayer operating at 10.3 bar, 5.1 km·h⁻¹, and 935 L·ha⁻¹ in a 4.0 m tall mandarin (Citrus reticulata) orchard using a U.S. Environmental Protection Agency (EPA)-approved, International Organization for Standardization (ISO) standard 22866-aligned protocol. Drift collectors (n = 2688), including flat cards, artificial foliage, and horizontal and vertical string samplers, were deployed from 33 m upwind to 183 m downwind of the orchard edge. Airborne drift measurements showed no significant vertical stratification or near-field decay between 8 m and 23 m downwind (p > 0.05), indicating rapid plume homogenization following canopy exit. In contrast, sedimentation drift declined sharply within 30 m and attenuated logarithmically with distance, governed by progressive droplet depletion and plume dilution. Estimated drift cessation distances were 127.5 m for artificial foliage and 182.1 m for horizontal string samplers. Drift magnitude varied significantly among trials (p < 0.05), reflecting sensitivity to meteorological variability. Multiple linear regression identified wind direction, wind speed, and atmospheric pressure as significant predictors of downwind deposition (p < 0.05), whereas air temperature and relative humidity primarily influenced drift through evaporative control of droplet lifetime. Collectively, these results demonstrate that spray drift from foliated citrus canopies is substantially attenuated relative to dormant-canopy scenarios. Although not intended to define regulatory buffer distances, the high-resolution dataset generated provides mechanistically interpretable parameterization inputs for next-generation airblast drift models, supporting improved representation of canopy interactions, plume evolution, and meteorological modulation in regulatory exposure assessments. Full article

The Patagonian huemul (Hippocamelus bisulcus) and the Southern pudu (Pudu puda) are native South American cervids that differ in body size, ecology, and conservation status. However, quantitative evidence linking vertebral trabecular microarchitecture with biomechanical behavior in these species remains scarce. This study aimed to comparatively characterize vertebral trabecular bone structure and its mechanical response using an integrative, non-destructive approach. Vertebral bodies from cervical, thoracic, and lumbar regions were analyzed using high-resolution micro-computed tomography to quantify structural parameters, followed by finite element analysis to estimate deformation and von Mises stress under standardized axial compression. Both specimens exhibited consistent regional variation, with cervical vertebrae showing lower density and organization, and thoracic–lumbar vertebrae displaying denser trabecular networks. The Southern pudu specimen appeared to present a more homogeneous microarchitecture and a relatively uniform mechanical response along the vertebral column. In contrast, the Patagonian huemul specimen tended to show greater structural heterogeneity, with apparently higher deformation and stress values, particularly in the cervical region. These findings suggest that trabecular organization may contribute to the differences in vertebral mechanical behavior observed between the analyzed specimens. This study provides a preliminary comparative baseline for understanding skeletal adaptation and structural vulnerability in South American cervid species. This exploratory analysis is based on single specimens per species and should be interpreted as preliminary evidence rather than population-level inference. Full article

Lensless digital holography provides a simple, low-cost imaging platform with a large field of view (FOV) and quantitative phase capability, making it attractive for biomedical imaging, microstructure inspection, and large area imaging. However, the achievable FOV is still limited by sensor size, and in-line reconstruction suffers from twin-image artifacts that degrade image quality. To overcome these limitations, this study proposes an extended FOV lensless digital holography method that combines hologram stitching with multi-depth phase retrieval. Multiple holograms acquired from laterally shifted FOVs are stitched to form an extended hologram, while holograms recorded at multiple axial depths are used to suppress twin-image artifacts and improve reconstruction fidelity. Experimental results show that the proposed method effectively expands the imaging area, enhances effective resolution by integrating complementary diffraction information from different FOVs, and improves image contrast and feature visibility. This approach enables extended FOV, resolution enhancement, and high-quality holographic imaging while preserving the simple lensless digital holography architecture. Full article

Background/Objectives: Osteonecrosis of the femoral head (ONFH) is difficult to diagnose, particularly in the early stages, because radiological findings may be subtle. Delayed or inaccurate staging may increase the risk of femoral head collapse and functional loss. Although magnetic resonance imaging is highly sensitive for early-stage lesion detection, interpretation may vary depending on observer experience. Therefore, reliable and explainable automated decision support approaches are needed. Methods: In this study, a deep learning-based approach was proposed to classify ONFH into early and late stages according to the Ficat–Arlet staging system. Stage I–II cases were defined as early-stage, whereas Stage III–IV cases were defined as late-stage. Axial and coronal MR images were evaluated separately to investigate plane-dependent classification performance. The images were converted into a three-channel format, resized to a common spatial resolution, normalized, and augmented during training. Feature extraction was performed using transfer learning with modern convolutional neural network architectures. ConvNeXt Tiny was used as the main classification backbone. Weighted loss was applied to reduce the effect of class imbalance, and the decision threshold was optimized on validation data to reduce missed clinically critical late-stage cases. Results: A dataset collected from the Orthopedics and Traumatology Department of Firat University Hospital was used in the experimental evaluation. The dataset was divided into training and test sets using an 80:20 split, and 10-fold cross-validation was additionally performed to assess model stability. In the hold-out test, the axial plane model achieved 94.51% accuracy, 96.80% sensitivity, 93.49% specificity, 0.9162 F1-score, and 0.981 AUC. In the coronal plane model, 92.84% accuracy, 96.13% sensitivity, 90.96% specificity, 0.9072 F1-score, and 0.988 AUC were obtained. The 10-fold cross-validation results provided a more conservative estimate of generalization performance. Conclusions: The findings indicate that deep learning-based plane-wise analysis of MR images can distinguish early- and late-stage ONFH with high performance. Grad-CAM-based visual explanations showed that the model focused mainly on clinically relevant subchondral and weight-bearing regions of the femoral head. The proposed approach may serve as an explainable decision support tool for reducing observer-dependent variability in clinical staging. Future studies should validate the method using external, multicenter datasets and paired patient-level axial–coronal images. Full article

Accurate measurement of the 2D soot spatial distribution is vital for optimizing combustion efficiency and reducing pollutant emissions. While 1D laser extinction (LE) is robust and cost-effective, it provides only line-of-sight integrated information, lacking the spatial resolution required to resolve complex soot topologies. We propose Soot-MixFormer, a hybrid deep learning model designed for the high-fidelity inversion of 2D soot distributions from 1D extinction data. The architecture integrates CNN-based local feature extraction with Transformer-based global dependency modeling. Key innovations include a dynamic decoupled generation head and a Dual-Axial Gated Refinement (DAGR) module coupled with a physical hard constraint layer to ensure mass conservation and physical consistency. Experimental results demonstrate that Soot-MixFormer significantly outperforms baseline MLP and CNN models, achieving a Structural Similarity Index (SSIM) of 0.800 and a Pearson Correlation Coefficient (PCC) of 0.915, and a highly suppressed Root Mean Square Error (RMSE) representing less than 10% relative error in high-concentration zones. Furthermore, the model exhibits exceptional robustness, maintaining a cosine similarity above 0.72 even under 10% simulated measurement noise. The model is highly efficient, with only 0.97 M parameters and a real-time inference speed of ~246 FPS. This study provides a novel, low-cost diagnostic paradigm for real-time, high-accuracy monitoring of soot fields in industrial combustion environments, effectively bridging the gap between simple 1D sensing and complex 2D spatial reconstruction. Full article

Mangrove represent vital coastal ecosystems that contribute to shoreline stabilization, ecological balance, and environmental management. Nevertheless, the precise delineation of mangrove regions using remote sensing data is often impeded by spectral similarities with intertidal mudflats and aquatic features, alongside the irregular spatial patterns and intricate margins of mangrove stands. This research utilizes high-resolution Gaofen-6 (GF-6) satellite observations as the foundational data to develop Triplet Axial High-Resolution Network (TAHRNet), a semantic segmentation architecture derived from the High-Resolution Network with Object-Contextual Representations (HRNet-OCR) framework for mangrove identification. The model integrates a Triplet Attention module to facilitate cross-dimensional feature dependencies and an improved Multi-Head Sequential Axial Attention mechanism to capture long-range spatial context while maintaining structural consistency. Based on evaluations using the test dataset, TAHRNet yielded a Mean Intersection over Union (MIoU) of 92.01% and a Overall Accuracy of 96.38%. Relative to U-Net and SegFormer, the proposed approach showed MIoU improvements of 5.25% and 1.88%, with corresponding Accuracy gains of 2.68% and 0.94%. Further application to coastal mapping in Zhanjiang produced results that align with manual visual interpretation. These findings suggest that TAHRNet is a viable tool for mangrove extraction and can provide technical support for coastal monitoring and ecological analysis. Full article

Ultrahigh lateral resolution (UHLR) optical coherence tomography (OCT) technology, also called optical coherence microscopy (OCM), has gained popularity, especially in the field of biomedical imaging. In these systems, high numerical aperture (NA) Microscope objectives (MO) are employed in OCM systems to offer better than 3 µm lateral resolution. However, in the implemented broadband OCM configuration, the use of complex multi-element microscope objectives can reduce the detected returned signal compared with a simpler imaging lens configuration. This reduction in detected returned signals can become an important practical limitation in many OCM applications, particularly for biomedical imaging when high imaging speed is crucial. This study investigates whether a single off-the-shelf lens can provide a practical alternative to conventional MOs, achieving higher throughput while maintaining reasonable spatial resolution. We systematically evaluated 14 commercial lenses using Zemax OpticStudio simulations, identifying an aspherized achromatic lens (Edmund Optics #85302) that best met these key criteria. To validate its feasibility for OCM, performance was tested in both Full-Field Time-Domain OCM (FF-TD-OCM) and Line-Field Spectral-Domain OCM (LF-SD-OCM) configurations. Using a broadband composite Superluminescent Diode (SLD) source (750–920 nm), we quantified the resolvable features, axial resolution, and overall light transmission. The validated system demonstrated near-diffraction-limited performance. In the LF-SD-OCM setup, it successfully resolved features as fine as Group 8, Element 6, corresponding to a 2.2 µm line pair pitch (~1.1 µm line width) and achieved a 2.86 µm axial resolution in air. A through-focus comparison further showed practically useful contrast retention around focus. Additional imaging of onion epidermal tissue and ex vivo porcine corneal tissue demonstrated that the proposed lens could provide interpretable structural images on representative biological samples. Under the tested LF-SD-OCM detection configuration, the selected lens delivered approximately 2.0 dB higher returned signal than the Mitutoyo MY10X-823 objective according to 1.59× larger received signal. 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