Search Results (3,480)

Fibromyalgia Syndrome (FMS) is a chronic syndrome commonly characterized by widespread musculoskeletal pain and fatigue. Current evidence suggests that FMS diagnosis relies on clinical evaluation and patient-reported symptoms. MicroRNAs, which serve as key regulators of gene expression, have been proposed to play a role in the pathogenesis of FMS and other chronic pain syndromes. In this pilot study, miRNA-223-3p expression levels were examined in patients with FMS, and their relationship with pain intensity—assessed using the Visual Analog Scale (VAS)was evaluated. To obtain a broader understanding of the inflammatory response, serum interleukin-1 beta (IL-1β) levels were also measured. miRNA-223-3p expression levels were significantly reduced in the FMS group compared with healthy controls (p < 0.05), whereas IL-1β levels did not differ significantly between the groups (p = 0.1135). The negative correlation between miRNA-223-3p and VAS scores indicates that lower miRNA levels are associated with increased pain severity. Overall, these results suggest that reduced miRNA-223-3p expression levels may be linked to neuroimmune processes and heightened pain perception in FMS. The findings provide valuable preliminary insights that may guide future studies with larger sample sizes. Full article

In UAV visual localization applications, the quality of image matching directly affects both the precision and reliability of the visual localization task. In UAV visual localization tasks, high-resolution remote sensing images are typically used as reference maps, whereas UAV-acquired aerial images serve as real-time inputs, enabling the estimation of the UAV’s spatial position through image matching. However, due to the substantial difference in imaging mechanisms and acquisition conditions between reference and real-time images, heterogeneous image pairs often contain numerous outliers, which significantly hinder the direct application of traditional matching algorithms such as RANSAC. To address these challenges, a spatial consistency-guided sampling algorithm is proposed. First, the initial correspondences are constructed based on triplet relationships, and their structural features are subsequently extracted. Then, a minimal subset sampling strategy is developed to improve sampling efficiency. Next, a data subset refinement strategy is introduced to further improve the robustness of sampling. Finally, extensive comparative experiments are conducted on the University-1652 and DenseUAV public datasets against several state-of-the-art feature matching algorithms. The experimental results demonstrate that the proposed algorithm achieves superior performance in correct matching rate, substantially enhancing the matching performance in heterogeneous image matching. Moreover, the proposed algorithm requires approximately 0.15 s per matching on average, and while maintaining the highest matching accuracy, it exhibits significantly higher computational efficiency than advanced sampling algorithms such as TRESAC and RANSAC, demonstrating strong potential for real-time applications in UAV visual localization tasks. Full article

Due to the large mass, high end load, and long action distance of a mill relining manipulator, gravity effects inevitably lead to a reduction in end effector positioning accuracy. To solve this problem, an online calculation method is proposed to realize real-time end effector deformation prediction. First, a manipulator is simplified into two cantilever beams: the upper arm and the forearm. Second, a reaction force and moment transformation model is established based on the coupling relationship between the forearm and upper arm. Third, finite element (FE) static analysis and simulation are carried out to obtain the end deformation. A total of 3528 discrete joint configurations are selected to cover the entire joint space, and their corresponding FE solutions are used to establish the end deformation offline dataset. Finally, an online deformation calculation algorithm based on Kolmogorov–Arnold networks (KANs) is developed to predict end deformation in any working condition. Visualization analysis and validation experiments are conducted and demonstrate the superiority of the proposed method in reducing gravity effects and improving computational efficiency. In summary, the proposed method provides support for end position compensation, especially for heavy-duty manipulators. Full article

Background and Objectives: The aim of this study was to evaluate the relationship between low back pain questionnaires and radiological stenosis severity in patients with lumbar spinal stenosis (LSS). Materials and Methods: Patients aged 50 years and over who presented with complaints of low back pain and were diagnosed or not diagnosed with LSS by magnetic resonance imaging (MRI) were included in the study. Demographic data, physical examination findings, and walking distance were recorded. Pain severity was assessed using the Visual Analog Scale (VAS), and patients completed the Oswestry Disability Index (ODI), the Istanbul Low Back Pain Disability Index (ILBPDI), and the Swiss Spinal Stenosis Questionnaire (SSS-Q). Results: A total of 120 patients with LSS (n = 56) and without LSS (n = 64) were included in the study. No significant differences were found between the groups in terms of demographic variables (p > 0.05). Neurogenic claudication and lumbar extension limitation were higher in the LSS group (p = 0.033 and p = 0.008, respectively), and walking distance was significantly shorter compared to the group without LSS (p = 0.024). There were significant differences between the VAS, ODI, ILBPDI, and SSS-Q scores between the two groups (p < 0.05). A strong positive correlation exists between the radiological severity of LSS and SSS-Q (p < 0.001, r = 0.707). Additionally, ROC analysis revealed that the SSS-Q had a significantly higher diagnostic value for LSS compared to the ODI and ILBPDI (p < 0.001). For the SSS-Q, likelihood ratios indicated limited diagnostic relevance (PLR 4.04 [95% CI: 2.45–6.67]; NLR 0.22 [95% CI: 0.13–0.44]). Conclusions: SSS-Q, ODI, and ILBPDI scores vary significantly between patients with and without LSS. Although the SSS-Q correlates most strongly with radiological LSS severity, its diagnostic utility appeared of minor importance, as likelihood ratios indicated limited discriminative ability. Full article

Traditional visual quality assessments of rural landscapes rely on subjective methods. This study integrates eye-tracking technology with subjective perception evaluation to construct a visual quality assessment model for rural landscapes, aiming to reveal the intrinsic relationship between objective visual behavior and subjective perception, with the aim of providing scientific guidance for rural landscape planning to promote sustainable rural development. Using landscape photographs from nine rural sampling sites in Guangzhou, eye-tracking experiments were conducted to collect participants’ eye movement data, combined with online questionnaires to obtain scenic beauty ratings and landscape characteristic factor evaluations. The findings reveal the following: (1) Eye-tracking experiments and subjective evaluation results showed high consistency, with samples having higher scenic beauty ratings demonstrating more prominent performance in core eye movement indicators such as total fixation duration and count, and total saccade duration, and typically possessing higher landscape characteristic factor values. (2) Urban–suburban-integrated rural landscapes exhibited poorer visual quality, characteristic-preservation rural landscapes elicited more in-depth and sustained visual exploration, and clustered-improvement rural landscapes possessed higher scenic beauty ratings and landscape characteristic factor values. (3) Total saccade duration was the key eye movement indicator for predicting scenic beauty ratings. (4) Multiple landscape characteristic factors significantly influence eye movement behavior. Full article

Tight reservoirs are highly heterogeneous, with complex pore-throat structures and varying fluid occurrences. The Archie equation shows a nonlinear relationship, making traditional logging interpretation methods unreliable for accurately predicting water saturation. This paper employs particle swarm optimization (PSO), using Pearson correlation coefficient-based feature selection, to compare the accuracy of three machine learning algorithms: XGBoost, LightGBM, and MERF in predicting water saturation in tight reservoirs. It also applies the SHAP value algorithm to provide a visual and interpretive analysis of the PSO LightGBM model. The research results indicate that the root mean square error (RMSE), coefficient of determination (R2), and accuracy of water saturation (Swa) of the PSO-LightGBM model on the training and test sets are 0.955, 3.087, 91.8%, and 0.89, 5.132, 85.2%, respectively. Interpretability analysis using SHAP values reveals that the five normalized logging parameters—SP, M2R3, DEN, DT, and CN—are the most influential features in the water saturation prediction model. In application examples involving water saturation prediction across eight sections of tight reservoirs in the study area, the PSO–LightGBM, PSO–XGBoost, and PSO–MERF models achieved Swa of 88.9%, 80.3%, and 87.8%, respectively. The results demonstrate that the PSO–LightGBM model is a reliable and efficient method for predicting water saturation, with significant practical potential. Full article

Understanding how the brain encodes and decodes dynamic neural responses to visual stimuli is crucial for revealing visual information representation. Currently, most methods (including deep neural networks, DNNs) often overlook the dynamic generation process of neural data, such as hierarchical visual data, within the brain’s structure. In this work, we introduce two decoding paradigms: fine-grained decoding tests (single brain regions) and coarse-grained decoding tests (multiple regions). Using the Allen Institute’s Visual Coding Neuropixel dataset, we propose the Adaptive Topological Vision Transformer (AT-ViT), which exploits a biologically calibrated cumulative hierarchy derived from single-area decoding performance to adaptively decode topological relationships across brain regions. Extensive experiments confirm the ‘Information-Gradient Hypothesis’: single-area decoding accuracy should recovers the anatomical visual hierarchy, and AT-ViT achieves maximal performance when this data-driven gradient is respected. AT-ViT outperforms non-hierarchical baselines (ada-PCA/SVM) by 1.08–1.93% in natural scenes and 2.46–3.34% in static gratings across sessions, peaking at hierarchy 3 (visual cortex + thalamus/midbrain) with up to 96.21%, but declining 1–2% when including hippocampus data, highlighting its random, performance-hindering nature. This work demonstrates hierarchical networks’ superiority for brain visual tasks and opens avenues for studying hippocampal roles beyond visual decoding. Full article

This study investigates fuel-induced oil dilution in hybrid powertrains using a combined assessment of kinematic viscosity and FTIR differential spectroscopy. Ten oil samples collected from hybrid vehicles operating under diverse real-world driving patterns were examined to determine how hybrid-specific operating conditions—such as frequent cold starts, extended start–stop phases and short, thermally unstable trips—influence lubricant ageing and, consequently, the energy efficiency of the combustion subsystem. In eight of the ten cases, a clear reduction in kinematic viscosity was observed, indicating the presence of volatile fuel fractions and confirming that fuel dilution is a dominant mechanism shaping the early stages of oil degradation in hybrid engines. FTIR analysis consistently revealed spectral shifts related to oxidation, nitration, sulfonation and additive depletion, together with hydrocarbon enrichment characteristic of fuel contamination. The co-occurrence of viscosity loss and FTIR band evolution demonstrates a strong and reproducible relationship between mechanical thinning of the lubricant and chemically driven transformation pathways, both of which can negatively affect frictional losses and energetic performance. Paper-based blot testing was used only as a supplementary qualitative tool and provided visual confirmation for samples exhibiting the strongest fuel-related FTIR signatures and viscosity changes. Although not mechanistically specific, the method reinforced the laboratory findings in cases of pronounced degradation. Overall, the results highlight the diagnostic value of combining viscosity data with FTIR spectral analysis to characterise fuel dilution and associated ageing mechanisms in hybrid combustion systems. This study contributes to a more comprehensive understanding of lubricant deterioration under real hybrid driving conditions and supports the development of practical monitoring strategies aimed at safeguarding both engine durability and the energy efficiency of hybrid powertrains. Full article

Glaucoma is one of the most common causes of irreversible visual impairment world wide. Providing low vision rehabilitation (LVR) services is a primary mode of support for patients with permanent vision loss. This retrospective, cross-sectional study evaluated the rate at which patients with severe open-angle glaucoma (OAG) were referred for LVR services at an academic medical center. Patient demographics, glaucoma severity, appointment history, performance on visual field (VF) testing, presenting visual acuity (VA), and change in best-corrected visual acuity (BCVA) after low vision refraction were abstracted from the electronic record and summarized by using descriptive statistics. Logistic regression analysis was used to assess the relationship between study variables and the likelihood of referral for LVR evaluation. Out of 522 patients with severe OAG, 88% of whom qualified as having low vision, 14 were referred for an LVR evaluation (2.7%). Referrals were most strongly associated with VA (adjusted odds ratio [aOR], 7.20; 95% confidence interval [CI], 2.11–24.64, p = 0.001) but not glaucoma-associated VF loss (aOR, 0.90; 95% CI, 0.24–3.37, p = 0.876). Thirteen of 14 patients referred for LVR completed visits (93%). More than one-third of those patients improved in their better-seeing eye after a low vision refraction, gaining an average of −0.18 ± 0.24 logMAR (half gaining ≥2-lines of BCVA). Patients with severe OAG are at risk of progressive visual disability from their eye disease. We found, however, that the majority of these patients were not referred to LVR services, despite meeting eligibility criteria and growing evidence demonstrating their potential benefit. Full article

Significant rural transformation has occurred in China’s formerly impoverished areas due to targeted poverty alleviation and rural revitalization strategies. In these areas, the coordinated development of the economic and ecological values of cultivated land resources is essential for rural transformation. This study focuses on the Liupan Mountain area, a typical poverty alleviation demonstration zone and Ecological and economic fragile area in Northwestern China. By collecting statistical yearbook data and raster data, it establishes a valuation system for cultivated land resources, transforming these resources into quantifiable poverty alleviation capital. This approach provides support for the long–term consolidation of targeted poverty alleviation policies. By integrating the Production Possibility Frontier (PPF) method with GIS spatial analysis, we developed a workflow to analyze value correlations and spatial patterns. The results showed the following: (1) While ecological values grew steadily from 2007 to 2022, economic value increased initially and then decreased, with both exhibiting significant spatial heterogeneity. (2) The relationship between economic value and ecological value evolved into a continuously strengthening synergy. (3) The integration of PPF curves with GIS visualization technology enabled the identification of underutilized, overutilized, and optimally utilized areas, revealing a distinct “π–shaped” overutilization zone. This study elucidates the trade–offs, synergies, and spatial characteristics of cultivated land values, providing critical insights for sustainable land resource management in post–poverty transformation areas. Full article

The topological structure of fracture networks fundamentally controls the mechanical behavior and fluid-driven failure of brittle materials. However, a systematic understanding of how topology dictates hydraulic fracture propagation remains limited. This study conducted experimental investigations on granite specimens containing 10 different topological fracture structures using a self-developed visual hydraulic fracturing test system and an improved Digital Image Correlation (DIC) method. It systematically revealed the macroscopic control laws of topological nodes on crack initiation, propagation path, and peak pressure. The experimental results indicate that hydraulic crack initiation follows the “proximal-to-loading-end priority” rule. Macroscopically, the breakdown pressure shows a significant negative correlation with topological parameters (number of nodes, number of branches, normalized total fracture length). However, specific configurations (e.g., X-shaped nodes) can exhibit a configuration-strengthening effect due to dispersed stress concentration, leading to a higher breakdown pressure than simpler topological configurations. Discrete Element Method (DEM) simulations revealed the underlying mechanical essence at the meso-scale: the topological structure governs crack initiation behavior and initiation pressure by regulating the distribution of force chains and the mode of stress concentration within the rock mass. These findings advance the fundamental understanding of fracture–topology–property relationships in rock masses and provide insights for optimizing fluid-driven fracturing processes in engineered materials and reservoirs. Full article

Background/Objectives: People with visual impairments can participate in orientation and mobility (O&M) training to learn how to navigate to their desired destinations. Instructors adapt their approach to each individual client. However, assessments of client characteristics and resulting instructional adaptations are not standardised and may therefore vary. This study aimed to identify which individual differences instructors consider during O&M training and why. Methods: We conducted semi-structured qualitative interviews with 10 O&M instructors. Participants were asked to describe how they prepare for a training trajectory, and to describe a route they taught a specific client. Thematic analysis was used to determine instructional choices and the relevant client-specific factors. Results: We observed a common four-step instructional process in which clients are taught to notice, interpret, act upon, and anticipate relevant sensory cues until a destination is reached. Four main themes captured the individual differences impacting this process: Sensory modalities, Capacities and limits, Personal contextual characteristics, and Training approach. Conclusions: Instructors perceive route learning to be shaped by clients’ sensory abilities (even fluctuating within sensory modalities), mental and physical capacities (especially concentration and energy), and personal characteristics (especially age and anxiety). The dynamic social context in which training takes place (e.g., the instructor–client relationship) is shaped by individual differences between both clients and instructors. We speculate that trust-related themes (e.g., building confidence) may explain why certain client characteristics are emphasised by instructors, as they are associated with training outcomes. Full article

Law Enforcement Agencies (LEAs) typically analyse vast collections of media files, extracting visual information that helps them to advance investigations. While recent advancements in deep learning-based computer vision algorithms have revolutionised the ability to detect multi-class objects and text instances (characters, words, numbers) from in-the-wild scenes, their association remains relatively unexplored. Previous studies focus on clustering text given its semantic relationship or layout, rather than its relationship with objects. In this paper, we present an efficient, modular pipeline for contextual scene text grouping with three complementary strategies: 2D planar segmentation, multi-class instance segmentation and promptable segmentation. The strategies address common scenes where related text instances frequently share the same 2D planar surface and object (vehicle, banner, etc.). Evaluated on a custom dataset of 1100 images, the overall grouping performance remained consistently high across all three strategies (B-Cubed F1 92–95%; Pairwise F1 80–82%), with adjusted Rand indices between 0.08 and 0.23. Our results demonstrate clear trade-offs between computational efficiency and contextual generalisation, where geometric methods offer reliability, semantic approaches provide scalability and class-agnostic strategies offer the most robust generalisation. The dataset used for testing will be made available upon request. Full article

Background/Objectives: This study systematically compared the performance of U-Net and variants for automatic analysis of carotid intima-media thickness (CIMT) in ultrasound images, focusing on segmentation accuracy and real-time efficiency. Methods: Ten models were trained and evaluated using a publicly available Carotid Ultrasound Boundary Study (CUBS) dataset (2176 images from 1088 subjects). Images were preprocessed using histogram-based smoothing and resized to a resolution of 256 × 256 pixels. Model training was conducted using identical hyperparameters (50 epochs, batch size 8, Adam optimizer with a learning rate of 1 × 10⁻⁴, and binary cross-entropy loss). Segmentation accuracy was assessed using Dice, Intersection over Union (IoU), Precision, Recall, and Accuracy metrics, while real-time performance was evaluated based on training/inference times and the model parameter counts. Results: All models achieved high accuracy, with Dice/IoU scores above 0.80/0.67. Attention U-Net achieved the highest segmentation accuracy, while UNeXt demonstrated the fastest training/inference speeds (approximately 420,000 parameters). Qualitatively, UNet++ produced smooth and natural boundaries, highlighting its strength in boundary reconstruction. Additionally, the relationship between the model parameter count and Dice performance was visualized to illustrate the tradeoff between accuracy and efficiency. Conclusions: This study provides a quantitative/qualitative evaluation of the accuracy, efficiency, and boundary reconstruction characteristics of U-Net-based models for CIMT segmentation, offering guidance for model selection according to clinical requirements (accuracy vs. real-time performance). Full article

Understanding outdoor thermal environments at fine spatial scales is essential for developing climate-responsive urban and building design strategies. This study investigates the determinants of local air temperature deviations in Seoul, Korea, using high-resolution in situ sensor data integrated with multi-source urban and building information. Hourly temperature records from 436 road-embedded sensors (March 2024–February 2025) were transformed into relative metrics representing deviations from the network-wide mean and were combined with semantic indicators derived from street-view imagery—Green View Index (GVI), Road View Index (RVI), Building View Index (BVI), Sky View Index (SVI), and Street Enclosure Index (SEI)—along with land-cover and building attributes such as impervious surface area (ISA), gross floor area (GFA), building coverage ratio (BCR), and floor area ratio (FAR). Employing an eXtreme Gradient Boosting (XGBoost)–Shapley Additive exPlanations (SHAP) framework, the study quantifies nonlinear and interactive relationships among morphological, environmental, and visual factors. SEI, BVI, and ISA emerged as dominant contributors to localized heating, while RVI, GVI, and SVI enhanced cooling potential. Seasonal contrasts reveal that built enclosure and vegetation visibility jointly shape micro-scale heat dynamics. The findings demonstrate how high-resolution, observation-based data can guide climate-responsive design strategies and support thermally adaptive urban planning. Full article