Search Results (8,618)

Background/Objectives: We aim to evaluate whether digital imaging and communications in medicine (DICOM)-level deep learning-based iodine-boosting applied to dual-source dual-energy computed tomography (DECT) source DICOM improves image quality in low-iodine-dose abdominal DECT in adults undergoing post-procedure follow-up computed tomography (CT). Methods: This retrospective study included 43 adults (April–September 2025) who underwent dynamic dual-source DECT using a low-iodine protocol. Three CT reconstructions were compared: mixed images, conventional 50-keV virtual monoenergetic images (VMIs), and 50-keV VMIs generated after applying DICOM-based deep learning iodine-boosting/denoising to the tube-specific dual-energy source DICOM series prior to VMI/iodine-map reconstruction (deep learning-based reconstruction [DLR]-VMI). Iodine material density (IMD) images were compared between the conventional and DLR-processed datasets. Quantitative attenuation and signal-to-noise ratio (SNR) were assessed using paired and repeated-measures tests. Image quality was scored by two readers using a five-point Likert scale. Results: Attenuation varied across CT reconstructions for all regions of interest in both phases (all overall p < 0.001). Liver attenuation increased from 94.9 ± 22.0 Hounsfield units (HU) (VMI) to 114.5 ± 34.6 HU (DLR-VMI) during the arterial phase and from 127.6 ± 25.6 HU to 166.6 ± 39.9 HU during the portal venous phase (both p < 0.001). Liver SNR improved with DLR-VMI compared to VMI (arterial: 9.11 ± 3.62 vs. 6.06 ± 1.90; portal: 12.74 ± 3.56 vs. 7.90 ± 1.82; both p < 0.001). On IMD images, DLR increased HU-equivalent values and liver SNR (arterial: 5.20 ± 2.89 vs. 2.61 ± 1.39; portal: 9.22 ± 2.81 vs. 4.48 ± 1.28; both p < 0.001). Qualitatively, DLR-VMI yielded the highest overall image-quality scores for both reviewers in both phases (Reviewer 1, arterial/portal: 4 (4–5)/5 (4–5); Reviewer 2, arterial/portal: 4 (3–4)/4 (4–4)). DLR also improved the overall image quality of IMD images for both reviewers (all p < 0.001). Conclusions: Raw DICOM-level iodine-boosting DLR applied to dual-source DECT-source DICOM enabled enhanced image quality and improved quantitative and qualitative metrics in low-iodine-dose abdominal DECT. Full article

Background/Objectives: To evaluate the influence of different DIXON contrasts on the quality of subtraction images in dynamic breast MRI using maximum intensity projections (MIPs). Methods: This retrospective study included n = 40 women (median age: 53.5 years, range 23–83) undergoing clinically indicated breast MRI (3T). For each MRI examination, two independent readers individually evaluated GBCA-enhanced subtraction MIPS for different timepoints (n = 5) and DIXON contrasts (n = 4) per breast, resulting in a total of 800 individual evaluations. Evaluations comprised (a) qualitative measures, using Likert-scores for artefact strength, breast parenchyma visibility, lesion visibility and reading confidence; and (b) signal intensity, measured in three regions of interest with the apparent signal-to-noise ratio (aSNR) and apparent contrast-to-noise ratio (aCNR) calculated. The evaluation results were analysed to identify differences between DIXON contrasts. Results: The “only water” DIXON contrast at ~120s after GBCA injection achieved the highest lesion conspicuity and reading confidence scores and lowest artefact scores; however, its performance was not statistically significant (p > 0.05) compared to the “in-phase” and “opposed-phase” subtractions. The aCNR at the second timepoint was slightly, but not significantly (p > 0.05), lower than the first timepoint, whilst aSNR increased significantly from the first to second timepoint in all contrasts. Conclusions: Subtraction MIPs derived from the “only water” DIXON contrast achieved the highest qualitative scoring for lesion conspicuity and confidence, with the aSNR increasing and aCNR decreasing between the first and second timepoints. Full article

This article investigates the housing precarity of EU migrant workers in the Dutch–German border region, focusing on the Venlo Greenport area. Drawing on documentary analysis, 28 interviews, field observations, and stakeholder engagement, it explores how local governance, market dynamics, and framing practices shape housing outcomes. While EU law guarantees free movement, housing remains excluded from the EU rights frameworks, leaving workers dependent on employer-linked or agency-controlled short-stay facilities. These arrangements—often overcrowded, surveilled, and formally temporary—become long-term solutions, producing what we term short-stay sedentarism: prolonged residence in housing designed to deny permanence. The study conceptualises the local “battleground” where municipalities, employers, housing providers, NGOs, and residents negotiate competing interests. Seven interpretive frames—nuisance/disorder, cowboys, human rights, NIMBY, shadow power, integration, and unwanted accumulation—structure these debates, legitimising certain strategies while obscuring structural deficiencies. Findings reveal that certification and enforcement, while intended to improve standards, often entrench precariousness by sustaining the short-stay model. Emerging integration-oriented policies signal a shift but remain fragile amid economic imperatives and spatial constraints. The paper argues that addressing housing precarity requires structural reforms: expanding access to regular housing, reducing employer dependency, and recognising migrant workers as long-term residents rather than temporary labour inputs. Full article

Meticulous segmentation of medical images requires obtaining both local and global spatial detailed information. The conventional U-Net model excels at local spatial feature extraction through residual convolutional blocks but struggles to capture global features. To resolve this issue, we propose the vision transformer U-NeT (ViTUNet) model framework, which combines the self-attention mechanism of the vision transformer (ViT) to capture global information while maintaining the extraction of local features via U-NeT. This proposed architecture introduces vision transformers to the existing residual convolution blocks in the U-Net encoder path, thereby capturing both local and global features. The decoder path then rebuilds this information into high-quality segmentation maps with accurately highlighted boundaries/edges. This model is utilized to segment carious lesions in bitewing dental radiographs. These images are pre-processed using augmentation, morphological operations, and segmentation to identify the boundaries/edges of the regions of interest (caries/cavity). The proposed method is evaluated on an augmented dataset containing 3000 image–watershed mask pairs. It was trained on 2400 training images and tested on 600 testing images. The experimental results exemplified significant improvements in segmentation performance, achieving $98.45\%$ validation accuracy, $97.88\%$ validation Dice coefficient, and $95.87\%$ validation intersection over union (IoU) metric scores. These results are superior compared to other conventional and state-of-the-art U-NeT models, thus highlighting the impact of transformer-based hybrid architectures in improving medical image segmentation tasks. Full article

Background: The recent U.S. Food and Drug Administration authorization of AirPods Pro as over-the-counter hearing aids (HAs) has increased interest in consumer devices as potential alternatives to traditional amplification; however, their electroacoustic performance relative to clinically fitted HAs remains unclear. The purpose of this study was to compare the electroacoustic characteristics and real-ear measures of AirPods Pro 2nd generation (APP2), AirPods Pro 3rd generation (APP3), and a traditional receiver-in-the-canal HA across mild flat, mild-to-moderate sloping, and moderate flat hearing loss configurations. Methods: Outcome measures included 2cc coupler output curves, saturation sound pressure level for a 90 dB input (SSPL90), real-ear speech mapping, maximum power output (MPO), and real-ear-to-coupler differences. Results: Coupler-based electroacoustic measures showed that APP2 and APP3 produced output comparable to the traditional HA (within 7 dB). SSPL90 outputs were similar for APP2 and APP3, whereas the HA demonstrated profile-dependent increases. In contrast, real-ear measurements demonstrated that both APP2 and APP3 consistently produced less output relative to the HA that was fitted to NAL-NL2 targets, with the largest deviations observed for moderate hearing loss and at higher frequencies (up to 14 dB). Across all configurations, MPO was consistently highest for the HA, with both AirPods devices exhibiting reduced maximum output, especially in speech-critical frequency regions. Real-ear-to-coupler difference findings indicated reduced acoustic coupling for APP3 relative to APP2 and the HA, contributing to reduced in-ear amplification despite comparable coupler outputs. Conclusions: While AirPods Pro may offer benefit for mild hearing loss or moderate high-frequency hearing loss, they do not provide output comparable to prescriptively fitted HAs. These findings underscore the continued importance of clinical verification and prescription-based fitting of hearing assistive technology for achieving appropriate audibility across hearing loss configurations. Full article

Demand for camel milk products is growing in Morocco and worldwide, creating opportunities to strengthen the livelihoods of populations living in arid regions through the development of camel-based dairy value chains. In addition to their economic potential, such value chains may contribute to sustainability by supporting food systems adapted to arid environments, promoting the use of locally resilient livestock species, and enhancing the socio-economic viability of vulnerable rural communities. This exploratory qualitative study investigates urban consumer behavior related to dairy consumption with a specific focus on the potential integration of camel milk products into local dietary habits. To capture nuanced consumer perspectives, gender-segregated focus-group discussions were conducted in three Moroccan cities using a semi-structured questionnaire on dairy consumption habits. Key factors examined included milk types, product preferences, purchasing locations, consumption frequency and willingness to include camel products in the household diet. The results indicate that camel milk is rarely consumed outside areas where camels are raised. Nevertheless, participants expressed interest in several camel milk-based products, particularly fermented milk and spreadable cheeses. This interest was primarily driven by perceptions of camel milk as a healthy product and by its association with traditional food practices. These findings suggest that expanding camel milk consumption in urban markets could support more sustainable and territorially rooted dairy systems by linking consumer demand with production models suited to dryland conditions. This study indicates promising market opportunities for the development of camel milk products in urban areas, particularly if challenges related to pricing strategies, distribution network, and region-specific supply chains are strategically managed. Full article

Ultra-precision thinning of hard and brittle materials like monocrystalline silicon demands high dynamic stability in thinning spindle. To address the challenge of accurately detecting subtle spindle chatter anomalies in industrial environments characterized by high noise and limited data, this paper proposes a physics-guided dual-stream attention fusion transfer network (PG-AFNet). First, a physics-guided signal preprocessing method was developed. Using variational mode decomposition (VMD) and continuous wavelet transform (CWT) masking, one-dimensional dynamic features and high-frequency regions of interest (ROIs) rich in transient impact features were extracted. Second, the PG-AFNet architecture was designed. By introducing an attention mechanism, it achieves deep integration of one-dimensional purely dynamic sequences with two-dimensional spatiotemporal visual textures to capture surface damage features caused by subtle vibrations. Finally, systematic validations were conducted using a real silicon wafer thinning dataset with 197 real samples. By overcoming small-sample limitations via physical augmentation, PG-AFNet achieved an 82.45% (86.64% after data augmentation) diagnostic accuracy, significantly outperforming traditional baselines. Furthermore, a large-scale cross-load validation on the diverse CWRU dataset yielded an exceptional 99.68% accuracy under mixed-load conditions, conclusively verifying the model’s robust domain generalization. Lastly, a rigorous ablation study explicitly quantified the indispensable contributions of the physics-guided dual-stream architecture and attention fusion. This research provides a feasible theoretical foundation for intelligent surface quality monitoring in semiconductor hard-brittle material processing. Full article

Background/Objectives: Migraine with aura (MwA) is a heterogeneous disorder comprising pure visual aura (MwAv) and more complex phenotypes with additional somatosensory and/or dysphasic symptoms (MwAvsd). Previous structural magnetic resonance imaging (MRI) studies have demonstrated hippocampal subfield volume reductions associated with aura complexity, suggesting a role for the hippocampus in MwA pathophysiology. However, functional network mechanisms underlying these structural differences remain unclear. This study aimed to investigate hippocampal resting-state functional connectivity (FC) in MwA subtypes and healthy controls (HCs), and to determine whether hippocampal connectivity patterns differ according to aura complexity. Methods: In this comparative cross-sectional study, 27 patients with MwAvsd, 18 with MwAv, and 29 age- and sex-matched HCs underwent resting-state functional MRI on a 3T scanner. Seed-based FC analyses were performed using both hippocampi as regions of interest. Results: MwAvsd patients demonstrated significantly increased FC between the right hippocampus and the left dorsal parietal cortex and right sensory association cortex compared with MwAv patients. In contrast, MwAv patients showed increased FC between the left hippocampus and the right dorsolateral prefrontal cortex compared with MwAvsd patients. Additionally, MwAv patients exhibited stronger FC between the left hippocampus and bilateral anterior prefrontal cortices and the left angular cortex compared with HCs. No other significant hippocampal FC differences were observed. Conclusions: Hippocampal FC is altered in MwA and varies according to aura phenotype. Complex aura is characterized by enhanced hippocampal coupling with multisensory integration regions and reduced connectivity with executive control areas, whereas pure visual aura demonstrates increased hippocampal–prefrontal and hippocampal–parietal associative connectivity compared with HCs. These findings suggest that the hippocampus might serve as a target for future neuromodulatory and therapeutic investigations in MwA patients. Full article

Spatial confinement strongly affects matter by altering structural stability, relaxation times, and equilibrium properties. Interest in hydrogen storage within carbon nanotube bundles has grown because it addresses practical energy needs while revealing rich confined-fluid physics. Understanding how geometry and defects influence hydrogen structure and dynamics is essential to the development of effective storage materials. Here, we investigate how confinement in single-walled carbon nanotube (SWCNT) bundles with vacancies alters the spatial distribution and phase behavior of physisorbed hydrogen. At low temperature, hydrogen forms solid-like, cylindrical layered structures both inside and outside the tubes. Raising the temperature broadens these layers and produces a liquid-like arrangement within the confined regions. This confined solid-to-liquid crossover controls storage capacity and release behavior and can be tuned by temperature, confinement dimensions, and vacancy defects. Full article

The genus Cirsium (family Asteraceae, subfamily Carduoideae) comprises more than 200 species distributed throughout the temperate regions of the Northern Hemisphere. In recent years, particular scientific interest has focused on Cirsium arvense (L.) Scop. (creeping thistle) and Cirsium vulgare (Savi) Ten. (spear thistle). These species are notable for their high content of secondary metabolites and broad biological activity. However, the available data on their phytochemical composition and biological potential remain fragmented. This information is methodologically diverse and scattered across different scientific disciplines, underscoring the need for systematic analysis. In this study, a comprehensive literature review was conducted. Sources included PubMed, Scopus, Web of Science, Google Scholar, and other online databases. The focus was on phytochemical composition and pharmacological activity. Both species contain a wide range of secondary metabolites. These include phenolic acids (chlorogenic, caffeic, and ferulic acids), flavonoids (luteolin, apigenin, kaempferol, quercetin), triterpenoids (lupeol, taraxerol), and phytosterols. C. vulgare generally has higher levels of chlorogenic acid and flavonoid glycosides. In contrast, C. arvense has a greater abundance of triterpenes and steroidal compounds. Pharmacological studies show antioxidant, antimicrobial, hepatoprotective, anti-inflammatory, and cytotoxic activities for both species. Overall, the available data indicate that C. arvense and C. vulgare are promising sources of biologically active compounds with diverse pharmacological potential. Although there are some limitations regarding standardization and the depth of preclinical and clinical validation, the obtained results confirm their relevance for further pharmacological and phytochemical research. Full article

With the increasing demand for high-quality imaging in consumer electronics, image aesthetics assessment (IAA) has been widely applied to electronic cameras and display devices. Although the deformable attention mechanism has been introduced into IAA due to its perceptual capabilities, enabling models to refine attention regions by learning interest points and their corresponding offsets, existing methods often lack guidance from aesthetic composition features during the offset generation process, which limits their performance in aesthetic evaluation tasks. To address this issue, we propose a graph neural network (GNN)-guided deformable attention module that incorporates composition information into the generation of interest points by modeling image features as graphs and applying the GNN to guide interest point selection. In addition, we design an improved transformer model that employs neighborhood attention to further enhance IAA performance. We evaluate the proposed model on two aesthetic datasets, AVA and TAD66K, and the experimental results demonstrate its effectiveness in improving overall model performance. Full article

Oil slicks pose a severe threat to marine ecosystems, making accurate and real-time detection increasingly urgent. Marine X-band radar has become an essential tool for oil slick monitoring due to its high temporal resolution and its ability to sensitively capture the damping of capillary waves on the sea surface caused by oil films. Building upon this, an unsupervised and lightweight SLIC-KMeans-GJO detection framework is proposed. The method first generates superpixels by using Simple Linear Iterative Clustering (SLIC) and then applies K-means clustering to extract region of interest (ROI). An improved Golden Jackal Optimizer (GJO) is adaptively initialized based on the grayscale distribution and information entropy. To enhance optimization performance, Lévy flight and stochastic perturbation mechanisms are incorporated to improve global exploration and local convergence precision. Experimental results demonstrate that the proposed method significantly outperforms conventional thresholding approaches and other intelligent optimization-based segmentation algorithms in terms of noise suppression, target identification accuracy, and discrimination precision for oil slick targets. It effectively mitigates over-segmentation and false detections while preserving fine edge details and the true spatial extent of oil slicks. The proposed framework offers a novel and practical solution for real-time oil slick monitoring, holding strong potential for operational maritime emergency response. Full article

Background: This study employed artificial intelligence (AI) to analyze quantitative nuclear morphological features obtained from digital pathology images to predict postoperative recurrence in patients with lung squamous cell carcinoma (LSQCC). We aimed to develop a prediction model that contributes to the realization of ‘personalized postoperative management’ tailored to individual tumor biology by integrating AI-extracted morphological features with clinical information. Methods: A total of 185 of the 253 surgically resected LSQCC cases were included; 136 were randomly assigned to the training set and 49 to the test set. Nuclear features from manually selected regions of interest were extracted and used to build AI-based prediction models. Three recurrence models were developed: recurrence within 2 years, within 5 years, and a three-category model (≤2 years, 3–5 years, >5 years or no recurrence). Support vector machine (SVM) and random forest (RF) algorithms were applied to each, yielding six predictive models. An ensemble approach was used to calculate AI-based risk scores, and a “total risk score” was developed by integrating these with the pathologic stage. Results: All six AI models demonstrated stable predictive performance, with AUC values ranging from 0.76 to 0.91. Kaplan–Meier analysis showed that the total risk score provided the most precise risk stratification (p < 0.005), with clearer separation between risk groups than the AI-based risk score alone. Conclusions: The integration of AI-based nuclear morphology analysis and clinical data provides an objective and practical tool for personalized postoperative management in LSQCC. This approach enables tailored clinical decision-making by identifying patients at high risk for early recurrence and customizing postoperative treatment plans to meet the specific needs of each individual. Full article

Background/Objectives: Perirenal fat (PRF) constitutes a critical yet understudied component of the tumor microenvironment in clear cell renal cell carcinoma (ccRCC). While radiomics enables non-invasive tissue characterization, whether PRF-derived features can simultaneously predict both TNM stage and histological grade, and whether optimal peritumoral distances differ between these distinct biological targets, remains unexplored in the literature. Methods: This multi-cohort retrospective study included 474 histopathologically confirmed ccRCC patients from three independent datasets (2007–2023). Automated nnU-Net segmentation delineated tumors and kidneys. Concentric PRF regions were systematically generated at 1–10 mm radial distances, yielding 18 distinct regions of interest. From each ROI, 1409 radiomic features were extracted using PyRadiomics. Sequential feature selection employed correlation filtering, SHAP-guided elimination, and LASSO regularization. Multiple machine learning classifiers underwent hyperparameter optimization with rigorous cross-cohort validation. Results: Systematic ROI screening revealed target-specific optimal distances: 4 mm PRF for TNM staging versus 10 mm PRF for histological grading. For staging, the integrated model (tumor + PRF radiomics + clinical variables) achieved AUC 0.829 (95% CI 0.781–0.877), sensitivity 80.2%, and specificity 67.8%. For grading, the combined model achieved AUC 0.780 (95% CI 0.598–0.962), sensitivity 79.7%, and specificity 63.3%, significantly outperforming all single-compartment models (DeLong p < 0.001). Conclusions: This study establishes that PRF radiomics enables accurate simultaneous non-invasive prediction of both TNM stage and histological grade in ccRCC. The novel discovery that optimal peritumoral distances differ substantially by prediction target (4 mm versus 10 mm) suggests distinct biological underpinnings for stage- and grade-related microenvironmental alterations, with important methodological implications for radiomic model development in oncology. Full article

This paper studies the sustainable integration of vernacular construction techniques and natural materials in the context of sustainable development, using Danube Delta UNESCO World Heritage site as case study. Through a comprehensive literature review, this research examines the potential of clay-based composites reinforced with plant fibres such as reed, bulrush, and hemp as environmentally responsible building materials. The methodology, based on a narrative literature review, combines bibliometric analysis with a case study approach to evaluate scientific interest in vernacular construction and to identify locally available natural resources. Results reveal increasing academic attention to sustainable vernacular architecture, highlighting clay-based composite’s favourable hygrothermal properties and the remarkable thermal insulation capabilities of vegetable fibres. The case study shows that most Danube Delta’s natural construction materials—particularly the world’s largest continuous reed vegetation—remain underutilized. The research concludes that revitalizing traditional construction methods, by integrating modern technological innovations, presents significant potential for sustainable rural development, preserving cultural heritage, enhancing regional identity, and reducing environmental impact in construction while supporting local economic growth through culturally authentic tourism. Full article