Search Results (108)

More than 90% of the road network of Loja, Ecuador, is unpaved and lacks an official condition metric. Low-cost measures relate to the IRI on pavements, but not demonstrably on unpaved roads. A 15 km Andean corridor was surveyed in one dry season with four visual–manual indices (URCI, MTC, PASER, ICNP), a Class III profilometer, and an uncalibrated smartphone application, over 150 stations harmonized from 60 native index observations, 30 for the MTC. A single profilometer run was moderately reliable (ICC = 0.631, 95% CI 0.559–0.699); four runs averaged 0.872, so repeated runs are a quantified requirement, not a convention. The application erred in level, not in spatial pattern (standardized ICC = 0.900), placing 88.7% of stations within one quartile of the reference: its role is segment-scale screening, not absolute IRI. Under leave-one-block-out spatial validation, the index–IRI equations retain an R² of 0.32–0.40; MTC was the only index whose association depended on the measuring instrument (p = 0.010). Surveying on foot cost 4.45–12.36 USD/km against 23.68 for reference profilometry, whose largest item is the vehicle. A tiered cost–accuracy architecture is proposed, with URCI and ICNP as preliminary candidates for official adoption, subject to validation based on further corridors and in wet condition. Full article

Accurate iris segmentation remains a fundamental challenge in iris biometric recognition and medical image analysis, particularly in challenging scenarios such as non-cooperative acquisition conditions involving variable illumination, partial occlusions, degraded image quality, and diverse unconstrained environments. Prevailing segmentation algorithms exhibit limited robustness when confronted with such challenges, and the disparity between near-infrared (NIR) and visible-light imaging modalities further compounds the complexity of achieving a robust segmentation outcome. To address these challenges, this paper introduces SwinIrisNet, a hybrid deep learning architecture that integrates Swin Transformer and convolutional neural network (CNN) branches within a U-Net framework for robust iris segmentation. The Swin Transformer branch leverages hierarchical window-based self-attention to capture global contextual dependencies, whereas the CNN branch extracts fine-grained local features essential for precise boundary delineation. A memory-efficient cross-attention fusion module combines these complementary feature representations, further enhanced by a Convolutional Block Attention Module (CBAM), Atrous Spatial Pyramid Pooling (ASPP), and attention-gated skip connections for multi-scale context aggregation. An extensive evaluation is conducted across four publicly available benchmark datasets, including UBIRIS.v2, IITD, CASIA-Thousand, and MMU.v1, encompassing both visible-light and NIR imaging environments. The proposed architecture yields F1 values of 0.9612–0.9672, Dice coefficients of 0.9489–0.9519, mIoU values of 0.9266–0.9450, precision values of 0.9565–0.9633, recall values of 0.9600–0.9672, and classification accuracies of 99.51–99.53%, with NICE1 error rates of 0.57–0.60% and NICE2 values of 1.82–2.24%, confirming pixel-level segmentation quality. Cross-database generalization experiments further demonstrate that SwinIrisNet learns transferable iris representations and generalizes effectively across heterogeneous imaging sources, with the strongest transfer occurring in the NIR-to-visible direction. A comparative analysis against existing algorithms demonstrates that the proposed architecture attains substantial performance improvements over several existing segmentation networks when evaluated on identical benchmark databases, surpassing them across the majority of qualitative and quantitative metrics while maintaining a marginally lower memory footprint. Full article

Although the prevalence of acute kidney injury and chronic kidney disease remains high and effective therapeutic targets remain scarce, significant progress has been made in recent years across the following major directions: G2/M phase cell cycle arrest, DNA damage, mitochondrial dysfunction, hypoxia-inducible factor signaling, dysregulated autophagy, and epigenetic alterations. RGC-32 is abundantly expressed in all tubular segments of normal renal tissues and is primarily localized to the cytoplasm and perinuclear region of renal tubular epithelial cells. Moreover, RGC-32 is involved in cell cycle regulation as well as cell proliferation and differentiation. To explore the functional role of RGC-32 in renal repair after acute ischemia–reperfusion injury, we utilized CRISPR-Cas9 technology combined with Cre/loxP recombination to generate a novel, renal tubule-specific RGC-32 knockout mouse model and systematically characterized its phenotype. Our findings demonstrate that renal tubule-specific RGC-32 deficiency does not impair normal growth or baseline renal function but alters the distribution of peripheral blood T lymphocyte subsets; whether this alteration contributes to renal immune regulation remains to be determined by future functional studies. More importantly, upon IRI, RGC-32 knockout in renal tubules leads to significantly aggravated renal dysfunction, elevated injury markers, and a possible association with enhanced chronic fibrosis. Full article

This paper presents an automated computational workflow for the acquisition, instrument-response correction and three-dimensional visualization of multi-component seismic records obtained from the IRIS Federation of Digital Seismograph Networks (FDSNs) using the open-source ObsPy (v1.5.0) package. The workflow targets engineering applications and consists of four stages: (i) selection of three-component (3C) broadband stations, (ii) bandpass filtering and spectral deconvolution of the instrument response to obtain ground displacement in physical units, (iii) calculation of theoretical P- and S-wave arrival times with the Tau-P kinematic algorithm based on the IASP91 reference Earth velocity model, and (iv) construction of an interactive 3D particle motion visualization in which segments associated with the P-wave, S-wave and background are color-coded. The pipeline is demonstrated on three seismic events recorded in February 2023 by the broadband station KO.BNN, including the destructive Mw 7.8 Kahramanmaraş earthquake. The workflow yields the absolute three-dimensional displacement vector and produces interactive visualizations that are intended for use by structural engineers as a complement to traditional one-dimensional acceleration records. Full article

Haul-road condition in open-pit mining affects fuel consumption, transport-cycle throughput and dump-truck mechanical loading. This study conducted a quasi-experimental field assessment of local maintenance effects on severely degraded road segments. The full cycle register contained 32,000 entries, of which 30,808 passed quality control and were used for background engineering characterization. The primary difference-in-differences (DiD) analysis used 2634 matched-control cycle–focal-segment observations, while 72,000 one-second telemetry records formed a separate 20 h dynamic validation subset. Relative to matched-control dynamics, maintenance increased RCI by 3.26 points and reduced IRI by 6.24 m/km, the apparent rolling-resistance descriptor by 2.71 percentage points and rut depth by 41.2 mm. Regression-adjusted effects were −10.6 L/cycle for fuel, −0.78 min for cycle time, +10.7 km/h for segment speed, −0.351 g for vibration RMS and −78.2 MPa-eq. for the suspension-stress proxy. A 14-day lead-placebo/event-time diagnostic found no detectable pre-intervention divergence, and non-negative cohort-weighted group-time ATT estimates closely matched the TWFE results. The coefficients are interpreted as site-specific local intervention-window effects rather than annual fleet-wide constants. Full article

In the context of pavement management, conventional data collection methods for the evaluation of pavement functional condition are limited by relatively slow acquisition speeds, that prevent fast-lane motorway surveying at 120–130 km/h, and by survey frequency, which on vast networks typically occurs twice a year. Given these limitations, continuous pavement condition monitoring from moving vehicles offers an attractive solution to move towards real-time digital road assessment. In particular, such a result is achieved by making use of “intelligent” or “smart” tires, which by means of appropriate arrays of sensors can capture contact patch information, thereby providing quantitative information related to pavement roughness and macrotexture. In this study, smart tire data functional condition indicators, Dynamic Index (DI) and Pr index, were collected over several segments of a motorway network, with a total length of 405 km. Correlations were investigated between such parameters and the results of measurements coming from a traditional pavement monitoring technique, expressed in terms of international roughness index (IRI) and mean profile depth (MPD). Furthermore, the ability of smart tire indicators to identify time-dependent trends and to rank different motorway segments was assessed. Obtained results, which were generated by adopting different data processing and homogenization techniques, showed that DI displays a moderate correlation with IRI, while Pr exhibits a strong correlation with MPD. Pavement-age analysis highlighted the existence of meaningful trends for both dense-graded and open-graded asphalt-wearing courses. Motorway rankings based on average DI and Pr values were found to be in agreement with those obtained from average IRI and MPD values, thereby confirming the potential of smart tire technology as a complementary network-level monitoring tool for pavement asset management systems. Full article

Portrait drawing is effective in cultivating artistic skills and visual understanding for a lot of people. Since drawing facial features with proper proportions, structures, and spatial relationships is hard for novices with no professional guidance, we developed the Portrait-Drawing Learning Assistance System, which offers auxiliary lines to assist in the drawing of portraits by acting as references; these auxiliary lines are extracted by applying OpenPose and OpenCV to a facial image. The drawing exactness assessment method using the Localized Normalized Cross-Correlation algorithm is also implemented to evaluate drawing accuracy. Unfortunately, previous experiments involving the use of the Portrait-Drawing Learning Assistance System by novices found that Localized Normalized Cross-Correlation scores for eyes are relatively low compared with others because of two drawbacks: (1) eye boundaries are often blurred by eyelids and eyelashes, which can make eyes bigger, and (2) the iris and pupil are not considered in auxiliary lines. In this paper, we propose an improved eye auxiliary line for the Portrait-Drawing Learning Assistance System to address these concerns. For the first drawback, we shift the lines for eye boundaries outside and add hatching (a series of short line segments) to them. For the second one, we expand region of interest of an eye and apply the Canny edge detector to capture its iris and pupil. In a preliminary within-subject evaluation, eight graduate students at Okayama University used the previous and proposed eye auxiliary lines in Procreate on an iPad. The average localized NCC scores increased for both eye regions under the tested conditions. These results suggest that the proposed auxiliary line may improve immediate eye-region reproduction accuracy; however, they do not constitute definitive validation of drawing-skill learning. Full article

Background/Objectives: To evaluate early postoperative changes in scleral and iris thicknesses together with corneal layer thicknesses and tomographic parameters following corneal collagen cross-linking (CXL) in eyes with progressive keratoconus. Methods: This retrospective study included 94 eyes of 94 patients with progressive keratoconus who underwent standard epithelium-off CXL using the Dresden protocol. Corneal tomography (Pentacam) and anterior segment optical coherence tomography (AS-OCT) measurements were obtained preoperatively and at the early postoperative follow-up (3 months ± 2 weeks). Thickness measurements of the tear film, corneal epithelium, Bowman layer, stroma, Descemet–endothelium complex, sclera (1–3 mm from the limbus), and iris (1–2 mm from the pupillary margin) were analyzed. Pre- and post-CXL values were compared using paired statistical tests, and effect sizes were calculated. Results: In the early postoperative period, scleral thickness showed a significant increase at all measured distances from the limbus, with medium effect sizes, while iris thickness demonstrated a significant decrease at all measurement points with large effect sizes (p < 0.001). Tear film, epithelial, and stromal thicknesses decreased significantly after CXL, whereas Bowman layer and Descemet–endothelium complex thicknesses remained unchanged. Pachymetric measurements revealed significant thinning at the pupil center, corneal apex, and thinnest point. No significant changes were observed in Kmax or anterior chamber depth, indicating stabilization rather than progression in the early postoperative period. Conclusions: Corneal collagen cross-linking was associated with measurable early structural changes in corneal layers and extra-corneal anterior segment tissues during the postoperative period. The observed increase in scleral thickness and decrease in iris thickness suggest that structural alterations may occur in extra-corneal anterior segment tissues following CXL. These findings support the concept that CXL influences anterior segment biomechanics in a tissue-specific manner and that extra-corneal parameters may serve as complementary markers for early postoperative assessment. Full article

This paper presents a dashboard-oriented visual analytics workflow for classification-related exploratory analysis based on Information Gain (IG), histogram segmentation, and complementary localized interpretation through the Precise Piecewise Correlation (PPC) method. The workflow is designed to support the construction of a primary dashboard view by prioritizing attributes with stronger relevance to the decision variable and inspecting their class-related behavior within segmented histogram intervals. Rather than introducing a new standalone feature-selection metric, this study formalizes how established analytical components can be integrated into a coherent dashboard framework for structured visual inspection. The proposed workflow was examined on three datasets from different application domains: the Iris dataset, an educational performance dataset, and an Oil and Gas dataset. Across these cases, IG-based prioritization identified attributes that provided clearer class-related structure in the primary dashboard view, while histogram segmentation supported interval-level interpretation of class concentration and overlap. A compact quantitative evaluation further showed that top-ranked IG subsets retained strong discriminative information under standard classification models, whereas lower-ranked subsets generally performed less favorably. Entropy-based segment analysis additionally indicated lower local class uncertainty for higher-ranked attributes. A small user study provided preliminary user-centered support for the interpretability and practical usefulness of the proposed dashboard structure. The results suggest that the proposed workflow can support dashboard-based inspection of class-related patterns across different contexts. Full article

Background: Branch retinal vein occlusion (BRVO) represents a segmental retinal ischemic disorder characterized by localized oxidative–inflammatory activation. While redox-driven cytokine responses have been described in central retinal vein occlusion, their role in BRVO-specific macular edema and treatment responsiveness remains unclear. This study investigated whether novel redox-related inflammatory mediators in the aqueous humor are associated with disease severity and structural response to anti-vascular endothelial growth factor (VEGF) therapy in BRVO. Methods: Aqueous humor samples were collected from 30 treatment-naïve patients with BRVO and 19 control patients. Levels of VEGF and the novel redox-related inflammatory factors FMS-related tyrosine kinase 3 ligand (Flt-3L), fractalkine, CXCL-16, and endocan-1 were measured by suspension array, and the severity of macular edema was evaluated by measuring central macular thickness and neurosensory retinal thickness (TNeuro) by spectral-domain optical coherence tomography. Therapeutic response was assessed one month after intravitreal ranibizumab injection (IRI). Results: Aqueous levels of VEGF, Flt-3L, and endocan-1 were significantly higher in the BRVO group, and levels of Flt-3L, CXCL-16, and endocan-1—markers associated with oxidative endothelial damage and leukocyte recruitment—correlated significantly with each other and with aqueous flare values. Notably, baseline Flt-3L levels significantly correlated with the reduction in TNeuro, suggesting that this redox-sensitive signaling molecule is a potential biomarker for treatment sensitivity. Conclusions: These findings suggest that novel inflammatory factors, potentially driven by oxidative-nitrosative stress, play a pivotal role in the pathophysiology of BRVO. Baseline Flt-3L may serve as a predictive biomarker for structural responsiveness to anti-VEGF therapy in BRVO, suggesting that oxidative–inflammatory signaling contributes not only to disease severity but also to therapeutic heterogeneity. Full article

High-frequency (HF) skywave propagation exploits ionospheric reflection for beyond-line-of-sight transmission, making time-difference-of-arrival (TDOA)-based geolocation a primary technique for localizing non-cooperative HF emitters. However, reliable TDOA estimation remains challenging due to time-varying ionospheric conditions, wideband multipath dispersion, and low signal-to-noise ratio (SNR). This paper proposes an integrated framework coupling realistic channel synthesis, deep learning-based TDOA estimation, and convex optimization-based localization. Three contributions are made. First, an improved wideband ionospheric channel model is constructed by integrating the International Reference Ionosphere (IRI) with region-specific calibration and a stochastic perturbation module, yielding time-varying multipath responses for physics-consistent waveform generation. Second, a convolutional neural network (CNN)-based TDOA estimator is designed to jointly exploit time-domain complex-baseband in-phase/quadrature (I/Q) waveforms, multi-weight generalized cross-correlation (GCC) feature maps, and channel-state information (CSI) within a unified regression network, achieving robust delay estimation under severe noise and multipath conditions. Third, the geolocation problem is formulated as a bias-regularized constrained least-squares problem with unknown ionospheric excess-delay surrogates, and a semidefinite programming (SDP) relaxation is derived to yield a tractable solution without prescribing a fixed virtual reflection height. Simulations show that the proposed estimator consistently outperforms competing algorithms across a wide SNR range and narrows the gap to the Cramér–Rao lower bound (CRLB) at high SNR. On field-recorded signals, the estimator reduces the mean absolute TDOA deviation by 51% relative to GCC with phase transform (GCC-PHAT), and the end-to-end pipeline achieves a mean geolocation error of 19.67 km across 100 field segments, outperforming all compared baselines. Full article

Descemet stripping automated endothelial keratoplasty (DSAEK) is a well-established surgical technique for the treatment of endothelial dysfunction, in which intracameral gas tamponade plays a critical role in graft adherence. We report the case of a 67-year-old pseudophakic woman with advanced Fuchs endothelial corneal dystrophy and symptomatic pseudophakic bullous keratopathy in the right eye, who presented with progressive visual deterioration and underwent DSAEK using an 8.25 mm donor graft inserted with a Busin glide and tamponaded with a 25% sulfur hexafluoride (SF6) gas–air mixture. On the first postoperative day, slit-lamp examination suggested an appropriate anterior chamber configuration and satisfactory graft attachment. However, detailed multiplanar anterior segment optical coherence tomography (AS-OCT), defined here as assessment using vertical, horizontal, and rotational scan orientations, revealed subtle posterior migration of the gas bubble beneath the iris plane. This clinically occult finding indicated altered anterior segment anatomy associated with a risk of secondary angle-closure mechanisms and raised concern for malignant glaucoma. Prompt surgical re-intervention was undertaken on postoperative day one, involving decompression of the misdirected gas bubble and reinjection of a centrally positioned tamponade. This resulted in restoration of normal anterior chamber configuration and stable graft adherence. Best-corrected visual acuity (BCVA) improved from 0.1 Snellen (1.0 logMAR) preoperatively to 0.7 Snellen (0.15 logMAR) at 2 weeks following surgery. This case highlights the added value of multiplanar AS-OCT in detecting clinically occult posterior gas migration after DSAEK, particularly when the abnormality is scan-orientation-dependent and not apparent on slit-lamp examination, thereby enabling timely intervention in the presence of a potentially sight-threatening postoperative configuration. Full article

Iris biometric systems are recognized as secure alternatives to conventional authentication methods, yet challenges such as variable illumination, noise, and intricate iris textures persist. To address these issues, our study presents a novel hybrid iris recognition framework that integrates advanced deep learning with a pioneering application of Multivariate Ensemble Empirical Mode Decomposition (MEEMD) for feature extraction—a method not previously applied in this context. Our framework first employs MEEMD to extract statistical features that capture the iris’s nonlinear and nonstationary variations. We then combine global semantic information from two pretrained convolutional neural networks—VGG16 and ResNet-152—with local micro-texture details encoded by Local Binary Patterns (LBP) to form a comprehensive feature representation. An efficient pre-processing and segmentation stage precisely isolates the iris region, and the resulting features are refined through dimensionality reduction techniques to yield a robust, compact representation. These features are subsequently classified using multiple models, each rigorously tuned via hyperparameter optimization. Experimental validation on benchmark datasets—including IITD, CASIA, and UBIRIS.v2—shows that our model achieves recognition rates of up to 98% on IITD, 97% on CASIA, and 97.30% on UBIRIS.v2, surpassing existing approaches. This work not only enhances iris recognition performance but also establishes a novel method that bridges advanced deep learning with innovative feature extraction for high-security applications. Full article

This study proposes a robust hybrid framework for iris segmentation in covert biometric systems, specifically addressing the challenge of non-ideal images featuring fully or nearly closed eyes. To overcome the limitations of traditional geometric methods, this study implements a SqueezeNet-based Deep Convolutional Neural Network (DCNN) for rapid eye-state classification. Comparative analysis with various pretrained DCNN models indicates that SqueezeNet provides an optimal balance of accuracy and efficiency, requiring only 1.24 million parameters and a minimal memory footprint of 5.2 MB. For iris contour demarcation, the proposed algorithm combines the Circular Hough Transform (CHT) with global gray-level statistics and anatomical constraints to facilitate reliable iris localization. Utilizing image decimation, percentile-based thresholding, and Canny edge detection, it systematically delineates the limbic and pupillary boundaries. This improved search methodology ensures precise contour delineation, even under sub-optimal imaging circumstances. The proposed algorithm was validated on a novel dataset encompassing challenging conditions such as specular reflections, blur, non-uniform illumination, and varying degrees of occlusion, including nearly or fully closed eyes. Experimental results demonstrate superior segmentation accuracy and significant computational efficiency, underscoring the model’s potential for real-time biometric applications in unconstrained environments. Full article

Objectives: To develop an effective learning method for using a smartphone-based slit-lamp microscope (SBSL) and to identify key points to emphasize when coaching individuals with no prior SBSL experience. Methods: This study included 60 orthoptic students: 40 second-year students (control group: 20, training group 1: 20) and 20 first-year students (training group 2). Subjects were instructed to record the anterior eye segment of a patient-role subject using the Smart Eye Camera. The control group was given paper instruction and was shown the demonstration of the SBSL beforehand. In addition, training groups 1 and 2 watched a tutorial video, practiced using the SBSL for 30 min, and received guidance from an expert. Four ophthalmologists evaluated the recordings based on the eyelid, conjunctiva, cornea, pupil including iris, lens, and anterior chamber depth. Results: ANOVAs showed significant differences among groups for all items. The control group had significantly lower scores than both training groups, while no significant differences were found between training groups 1 and 2. Principal component analysis of training groups 1 and 2 showed that the first principal component accounted for 74.36% of the variance. The second principal component accounted for 10.71%, with a wide range of loadings (anterior chamber depth of 0.7780 to conjunctiva of −0.5585), implying the existence of different favorite focusing depths within subjects. Conclusions: A coaching program consisting of tutorial video learning, a 30 min hands-on trial, and feedback is effective in helping individuals without an ophthalmological background acquire anterior segment imaging skills using SBSL. Comprehensive focusing across the entire anterior segment should also be emphasized. Full article