Search Results (115)

Underwater inspection images of hydraulic structures often suffer from haze, severe color distortion, low contrast, and blurred textures, impairing the accuracy of automated crack, spalling, and corrosion detection. However, many existing enhancement methods fail to preserve structural details and suppress noise in turbid environments. To address these limitations, we propose a compact image enhancement framework called Wavelet Fusion with Sobel-based Weighting (WWSF). This method first corrects global color and luminance distributions using multiscale Retinex and gamma mapping, followed by local contrast enhancement via CLAHE in the L channel of the CIELAB color space. Two preliminarily corrected images are decomposed using discrete wavelet transform (DWT); low-frequency bands are fused based on maximum energy, while high-frequency bands are adaptively weighted by Sobel edge energy to highlight structural features and suppress background noise. The enhanced image is reconstructed via inverse DWT. Experiments on real-world sluice gate datasets demonstrate that WWSF outperforms six state-of-the-art methods, achieving the highest scores on UIQM and AG while remaining competitive on entropy (EN). Moreover, the method retains strong robustness under high turbidity conditions (T ≥ 35 NTU), producing sharper edges, more faithful color representation, and improved texture clarity. These results indicate that WWSF is an effective preprocessing tool for downstream tasks such as segmentation, defect classification, and condition assessment of hydraulic infrastructure in complex underwater environments. Full article

The underwater environment severely degrades image quality by absorbing and scattering light. This causes significant challenges, including non-uniform illumination, low contrast, color distortion, and blurring. These degradations compromise the performance of critical underwater applications, including water quality monitoring, object detection, and identification. To address these issues, this study proposes a detail-oriented hybrid framework for underwater image enhancement that synergizes the strengths of traditional image processing with the powerful feature extraction capabilities of unsupervised deep learning. Our framework introduces a novel multi-scale detail enhancement unit to accentuate structural information, followed by a Latent Low-Rank Representation (LatLRR)-based simplification step. This unique combination effectively suppresses common artifacts like oversharpening, spurious edges, and noise by decomposing the image into meaningful subspaces. The principal structural features are then optimally combined with a gamma-corrected luminance channel using an unsupervised MU-Fusion network, achieving a balanced optimization of both global contrast and local details. The experimental results on the challenging Test-C60 and OceanDark datasets demonstrate that our method consistently outperforms state-of-the-art fusion-based approaches, achieving average improvements of 7.5% in UIQM, 6% in IL-NIQE, and 3% in AG. Wilcoxon signed-rank tests confirm that these performance gains are statistically significant (p < 0.01). Consequently, the proposed method significantly mitigates prevalent issues such as color aberration, detail loss, and artificial haze, which are frequently encountered in existing techniques. Full article

Hydraulic concrete is a critical infrastructure material, with its surface condition playing a vital role in quality assessments for water conservancy and hydropower projects. However, images taken in complex hydraulic environments often suffer from degraded quality due to low lighting, shadows, and noise, making it difficult to distinguish defects from the background and thereby hindering accurate defect detection and damage evaluation. In this study, following systematic analyses of hydraulic concrete color space characteristics, we propose a Dual-Branch Luminance–Chrominance Attention Network (DBLCANet-HCIE) specifically designed for low-light hydraulic concrete image enhancement. Inspired by human visual perception, the network simultaneously improves global contrast and preserves fine-grained defect textures, which are essential for structural analysis. The proposed architecture consists of a Luminance Adjustment Branch (LAB) and a Chroma Restoration Branch (CRB). The LAB incorporates a Luminance-Aware Hybrid Attention Block (LAHAB) to capture both the global luminance distribution and local texture details, enabling adaptive illumination correction through comprehensive scene understanding. The CRB integrates a Channel Denoiser Block (CDB) for channel-specific noise suppression and a Frequency-Domain Detail Enhancement Block (FDDEB) to refine chrominance information and enhance subtle defect textures. A feature fusion block is designed to fuse and learn the features of the outputs from the two branches, resulting in images with enhanced luminance, reduced noise, and preserved surface anomalies. To validate the proposed approach, we construct a dedicated low-light hydraulic concrete image dataset (LLHCID). Extensive experiments conducted on both LOLv1 and LLHCID benchmarks demonstrate that the proposed method significantly enhances the visual interpretability of hydraulic concrete surfaces while effectively addressing low-light degradation challenges. Full article

Background/Objectives: Unilateral hearing loss (UHL), particularly single-sided deafness (SSD), disrupts spatial hearing in children, leading to academic and social challenges. This study aimed to (1) compare azimuthal sound-localization accuracy and compensatory strategies between children with single-sided deafness (SSD) and their normal-hearing (NH) peers within a virtual reality environment, and (2) investigate sound-localization performance across various azimuths by contrasting left-SSD (L-SSD) and right-SSD (R-SSD) groups. Methods: A cohort of 44 participants (20 NH, 24 SSD) performed sound localization tasks in a 3D virtual environment. Unsigned azimuth error (UAE), unsigned elevation error (UEE), and head movement distance were analyzed across six azimuthal angles (−75° to 75°) at 0°elevation. Non-parametric statistics (Mann–Whitney U tests, Holm–Bonferroni correction) compared performance between NH and SSD groups and within SSD subgroups (L-SSD vs. R-SSD). Results: The SSD group exhibited significantly higher UAE (mean: 22.4° vs. 3.69°, p < 0.0001), UEE (mean: 5.95° vs. 3.77°, p < 0.0001) and head movement distance (mean: 0.35° vs. 0.12°, p < 0.0001) compared with NH peers, indicating persistent localization deficits and compensatory effort. Within the SSD group, elevation performance was superior to azimuthal accuracy (mean UEE: 3.77° vs. mean UAE: 22.4°). Participants with R-SSD exhibited greater azimuthal errors at rightward angles (45°and 75°) and at −15°, as well as increased elevation errors at 75°. Hemifield-specific advantages were strongest at extreme lateral angles (75°). Conclusions: Children with SSD rely on insufficient compensatory head movements to resolve monaural spatial ambiguity in order to localize sounds. Localization deficits and the effort associated with localization task call for action in addressing these issues in dynamic environments such as the classroom. L-SSD subjects outperformed R-SSD peers, highlighting hemispheric specialization in spatial hearing and the need to study its neural basis to develop targeted rehabilitation and classroom support. The hemifield advantages described in this study call for further data collection and research on the topic. Full article

Multimodal image fusion and semantic segmentation play pivotal roles in autonomous driving and robotic systems, yet their inherent interdependence remains underexplored. To address this gap and overcome performance bottlenecks, we propose SC-CoSF, a novel coupled framework that jointly optimizes these tasks through synergistic learning. Our approach replaces traditional duplex encoders with a weight-sharing CNN encoder, implicitly aligning multimodal features while reducing parameter overhead. The core innovation lies in our Self-correction and Collaboration Fusion Module (Sc-CFM), which integrates (1) a Self-correction Long-Range Relationship Branch (Sc-LRB) to strengthen global semantic modeling, (2) a Self-correction Fine-Grained Branch (Sc-FGB) for enhanced visual detail retention through local feature aggregation, and (3) a Dual-branch Collaborative Recalibration (DCR) mechanism for cross-task feature refinement. This design preserves critical edge textures and color contrasts for segmentation while leveraging segmentation-derived spatial priors to guide fusion. We further introduce the Interactive Context Recovery Mamba Decoder (ICRM) to restore lost long-range dependencies during the upsampling process; meanwhile, we propose the Region Adaptive Weighted Reconstruction Decoder (ReAW), which is mainly used to reduce feature redundancy in image fusion tasks. End-to-end joint training enables gradient propagation across all task branches via shared parameters, exploiting inter-task consistency for superior performance. Experiments demonstrate significant improvements over independently optimized baselines in both fusion quality and segmentation accuracy. Full article

This study aimed to comparatively evaluate the effects of various surface treatment protocols on the shear bond strength (SBS) between heat-polymerized polymethyl methacrylate (PMMA) and different CAD/CAM framework materials, including cobalt–chromium (Co–Cr) alloys, ceramic particle-reinforced polyetheretherketone (PEEK), and glass fiber-reinforced composite resin (FRC). A total of 135 disc-shaped specimens were prepared from Co–Cr, PEEK, and FRC materials. Surface treatments specific to each material, including airborne-particle abrasion, sulfuric acid etching, laser irradiation, plasma activation, and primer application, were applied. PMMA cylinders were polymerized onto the treated surfaces, and all specimens were subjected to 30,000 thermal cycles. SBS values were measured using a universal testing machine, and the failure modes were classified. The normality of data distribution was assessed using the Kolmogorov–Smirnov test, and the homogeneity of variances was evaluated using Levene’s test. Group comparisons were performed using the Kruskal–Wallis test, and Dunn’s post hoc test with Bonferroni correction was applied in cases where significant differences were detected (α = 0.05). The highest SBS values (~27–28 MPa) were obtained in the Co–Cr group and in the PEEK groups treated with sulfuric acid and primer. In contrast, the PEEK group with additional laser treatment exhibited a lower SBS value. The untreated PEEK group showed significantly lower SBS (~3.9 MPa) compared to all other groups. The Trinia groups demonstrated intermediate SBS values (16.5–17.4 MPa), which exceeded the clinically acceptable threshold of 10 MPa. SEM observations revealed material- and protocol-specific surface responses; plasma-treated specimens maintained topographic integrity, whereas laser-induced surfaces showed localized degradation, particularly following dual-step protocols. Fracture mode analysis indicated that higher SBS values were associated with cohesive or mixed failures. SEM observations suggested that plasma treatment preserved surface morphology more effectively than laser treatment. This study highlights the importance of selecting material-specific surface treatments to optimize bonding between CAD/CAM frameworks and PMMA. Sulfuric acid and primer provided strong adhesion for PEEK, while the addition of laser or plasma offered no further benefit, making such steps potentially unnecessary. Trinia frameworks also showed acceptable performance with conventional treatments. These findings reinforce that simplified conditioning protocols may be clinically sufficient, and indicate that FRC materials like Trinia should be more fully considered for their broader clinical potential in modern CAD/CAM-based prosthetic planning. Full article

All-optical matching systems that detect and localize designated target sequences in input all-optical data sequences have attracted significant attention in all-optical processing. They have various applications, including all-optical intrusion detection, optical frame alignment, and optical package identification. In real-world applications, noise is inevitable and can lead to incorrect matching results. In particular, noise accumulates in serial all-optical matching systems, rendering the systems useless after several cycles. In this study, we developed a scheme for suppressing noise in quadrature phase-shift-keying (QPSK)-oriented all-optical matching systems. First, we evaluated the impact of input and amplifier noise on a QPSK-oriented all-optical matching system at a transmission rate of 100 Gbaud. We then developed a second-order noise-suppression structure using a highly nonlinear fiber (HNLF). With an input optical signal-to-noise ratio (OSNR) of 6 dB and an amplifier noise figure (NF) of 4 dB, the QPSK-oriented all-optical matching system without the noise-suppression structure output incorrect results. However, when the system was optimized using the proposed noise-suppression structure, correct matching results were obtained. Furthermore, when the NF of the amplifiers was fixed at 4 dB, the optimized system could reduce the minimum input OSNR to 0 dB. With an input OSNR of 0 dB, the logarithm of the bit error rate (BER) of the output matching results of the optimized system tended to negative infinity. The extinction ratio (ER), contrast ratio (CR), and quality (Q) factor of the output of the optimized system were 154.9532, 166.94289, and 161.12 dB, respectively, indicating high noise resistance. These results demonstrate that the system optimized using the proposed noise-suppression scheme exhibits high stability and reliability in noisy environments. Full article

The properties of light propagation underwater typically cause color distortion and reduced contrast in underwater images. In addition, complex underwater lighting conditions can result in issues such as non-uniform illumination, spotting, and noise. To address these challenges, we propose an innovative underwater-image enhancement (UIE) approach based on maximum information-channel compensation and edge-preserving filtering techniques. Specifically, we first develop a channel information transmission strategy grounded in maximum information preservation principles, utilizing the maximum information channel to improve the color fidelity of the input image. Next, we locally enhance the color-corrected image using guided filtering and generate a series of globally contrast-enhanced images by applying gamma transformations with varying parameter values. In the final stage, the enhanced image sequence is decomposed into low-frequency (LF) and high-frequency (HF) components via side-window filtering. For the HF component, a weight map is constructed by calculating the difference between the current exposedness and the optimum exposure. For the LF component, we derive a comprehensive feature map by integrating the brightness map, saturation map, and saliency map, thereby accurately assessing the quality of degraded regions in a manner that aligns with the symmetry principle inherent in human vision. Ultimately, we combine the LF and HF components through a weighted summation process, resulting in a high-quality underwater image. Experimental results demonstrate that our method effectively achieves both color restoration and contrast enhancement, outperforming several State-of-the-Art UIE techniques across multiple datasets. Full article

India is a major producer of mandarin oranges. However, the average fruit yield remains below potential due in part to multiple nutrient deficiencies. Our objective was to elaborate compositional nutrient diagnosis (CND) log-ratio standards accounting for nutrient interactions and the dilution the leaf tissue. We hypothesized that equally or unequally weighted dual nutrient log ratios integrated into centered log ratios (clr) or weighted log ratios (wlr) influence the accuracy of the CND diagnosis. The database comprised 494 observations on ‘Nagpur’, ‘Khasi’, and ‘Kinnow’ cultivars surveyed in contrasting agroecosystems of India. Weights were provided by gain ratios that indicated the importance of the dual log ratio on crop performance. The cutoff yield was set at the upper high-yield quarter for each variety. Centered log ratios (clrs) and weighted log ratios (wlrs) returned accuracies of 0.7–0.8 depending on the machine learning classification model. The gain ratios were not contrasted enough to make a difference between clr and wlr. We derived clr and wlr nutrient standards following the Gradient Boosting model. In a case study, the clr and wlr returned similar diagnoses. The capacity of clr and wlr to generalize to unseen cases and correct nutrient imbalance should be further verified in fertilizer trials. The diagnosis could also be conducted at a local scale, thanks to the Euclidian geometry and additivity of clr and wlr variables. Full article

Plant species diversity and spatial distribution patterns are critical for understanding ecosystem dynamics in arid and fragile environments. This study investigates the diversity, spatial distribution, and interspecific associations of shrubs and herbaceous plants in the transition zone of the desert oasis located in the Hexi Corridor and southern edge of the Badanjilin Desert, China. Vegetation data were collected across sample plots spanning three counties in Zhangye City. Important values, diversity indices, and spatial distribution metrics were calculated to evaluate plant species dominance and community structure. Interspecific relationships were analyzed using variance ratio (VR), clumping indicators, and corrected χ² tests. The shrub community exhibited low species diversity (H′ = 1.754) and was dominated by Reaumuria songarica (Pall.) Maxim (IV = 111.175), reflecting its superior adaptability to arid conditions. In contrast, the herbaceous community displayed higher diversity (H′ = 2.498), with Aristida adscensionis L. (IV = 48.6174) as the dominant species. Both communities showed predominantly aggregative spatial distribution patterns, influenced by localized resource availability and adaptive strategies. Weak interspecific associations characterized the shrub community, with limited competition among dominant species, while the herbaceous community demonstrated significant negative correlations, indicating stronger resource competition. The study highlights the contrasting diversity and ecological roles of shrubs and herbaceous plants in arid ecosystems, shaped by resource limitations and environmental stressors. Effective conservation strategies are needed to protect dominant species and sustain ecosystem resilience in desert regions. Future research should focus on below-ground interactions and long-term monitoring to enhance understanding of species coexistence and community stability. Full article

Within the framework of the research project “What are we forgetting in inclusive education? In participatory research in Galicia (PID2019-108775RB-C4)”, we developed a proposal that, based on the inclusion and community perspective, investigates the relationship between municipal corporations and educational centres for the design and implementation of the programmes that was municipally developed to promote educational inclusion. The role of municipalities as promoters of inclusion through educational programmes is analysed, focusing on the mediation of third-sector institutions. It also studies the demands of the agents that comprise a community perspective on inclusion. The qualitative–interpretative methodology is based on SWOT (strengths, weaknesses, opportunities and threats) and CAME (correct, adapt, maintain and explore) techniques to contrast the congruence of existing initiatives with the needs of the educational community, synthesising information from 39 interviews. The results show deficient channelling of the schools’ demands into municipal programmes, which are mostly planned in collaboration with third-sector entities, without the participation of the school community. It is concluded that this demand for greater participation and collaboration is one of the main demands of the school community to promote inclusion at the local level. Full article

Background: Lesions of the digital apices are common, and several treatment strategies can be considered for them. Among these, the free great toe pulp flap can be used. Methods: This is a retrospective report in which five patients undergoing hallux free flap surgery for loss of pulpal substance at the level of the hand were evaluated. They were re-evaluated by using both clinical testing to assess sensitivity and the use of questionnaires to estimate function. Results: None of the performed flaps failed. The mean follow-up was 36 months (range 16–66 months). With SW-MF, the mean value was 3.734 compared to 2.986 for the same contralateral finger. The S2-PD test attested a mean value of 6.8 mm (range 6–8 mm) in contrast to the contralateral finger, which showed a mean result of 3.2 mm (range 3–5 mm), while the D-2PD indicated lower values for both the operated finger, with a mean value of 6.4 mm (range 4–8 mm), and the healthy finger. Conclusions: When a dystrophic fingertip results from an inappropriate acute management, the GTP flap appears to be an excellent strategy to restore the specialized tissue of finger pulp and to bring supple tissue to the correct PIP flexion contracture or the small first web space contracture. It is mostly required for thumb and radial fingers’ reconstruction, especially in young patients or those who need high functional demands and/or present an extensive loss of substance that cannot be resolved with local flaps. Full article

The structural gravimetry problem, which involves determining the geometry of a contact surface between two homogeneous layers based on observed gravity fields, is addressed in this paper. The method of local corrections is presented in a generalized form to improve its applicability to a broader range of problems. This study introduces several improvements to the local corrections method, including the use of a finite element approach for more accurate field calculations, particularly for near-surface boundaries. Additionally, the method incorporates prior knowledge of the boundary geometry, which serves as an initial approximation to enhance convergence and avoid potential divergence issues. Demonstrations on several synthetic examples are performed, which show the advantages of the generalized form of the method. For the territory of the Middle Urals, Russia, the refinement of two crustal boundaries is performed (the Moho boundary and middle crust boundary). Full article

Extreme rainfall-induced events adversely affect agriculture, infrastructure, and socioeconomic development in a region. Therefore, a comprehensive understanding of their occurrences and past and future variability in the context of global warming is imperative, especially at the fine temporal (sub-daily) and spatial (local to regional) scales for better contextualizing inferences from a policymaking perspective. This study provides a detailed analysis of global warming’s impacts on extreme rainfall in Jiangsu Province, utilizing the latest high-resolution ERA5-Land reanalysis data and the latest climate models. A novel temporal disaggregation model was developed to predict future hourly extreme rainfall. The results show that the bias-corrected model reduced the overestimation of extremes by as much as ~7.4% for the location parameter and accurately reproduced the spatial variability of rainfall. Projections from eight climate models indicate a future increase in rainfall intensity by an average of over 7%. Moreover, the projections indicate two contrasting trends for different event durations: short-duration events (e.g., 1 h) show a 7.1% increase at the 5-year return period and a more pronounced 8.9% increase at the 50-year return period. Conversely, long-duration events (e.g., 24 h) experience an 8.4% increase at the 5-year return period and a smaller 6.0% increase at the 50-year return period. This suggests that rarer, short-duration events are expected to increase more than less rare ones, while rarer, long-duration events show a smaller increase than their less rare counterparts. Addressing spatial heterogeneity in extreme rainfall patterns provides actionable insights for climate adaptation and mitigation, supporting initiatives like the ‘Jiangsu Province Climate Change Adaptation Action Plan’. This study underscores the need for policy-driven, community-led climate actions to mitigate flood risks and enhance resilience in a region vulnerable to flooding amidst global warming and increasing human interventions. Full article

This manuscript deals with research in the field of human resource efficiency in the operation of railway transport, which is currently a very actual and important topic. The correct efficiency and organization of the work of employees in railway operations have a significant impact on sustainable railway transport and the sustainable functioning of the transport sector. This research investigated two fundamental principles of railway transport operation control: local control and remote control. Local control involves physically managing transport processes from a traffic office within the station, with a focus on direct supervision. In contrast, remote control, which relies on optical cables, allows for system operation even during malfunctions. The article compares these control methods from technological and economic perspectives. Notably, local control requires a larger number of qualified employees, impacting efficiency. This research reveals that remote control, facilitated by a relay room and traffic office at each station, enhances teamwork, providing an immediate response to situations and enabling dynamic operational adjustments. Moreover, the article assesses the required personnel for optimal staffing, considering factors such as track configuration, departing trains, and reporting district size. Economic indicators, particularly wages, show significant savings with remote control, impacting stations with excluded passenger movement more pronouncedly. The findings highlight the efficiency and economic advantages of remote control in railway transport. The specific contribution of the research to the sustainability of transport and sustainable rail transport is presented in the discussion of the manuscript. Full article