Search Results (2,153)

Bicuspid Aortic Valve (BAV) disease is recognized as the most common congenital heart condition and is frequently associated with complex valvular and aortic disorders. Cardiovascular computed tomography (CT) has become essential for diagnosing BAV, planning procedures, and evaluating patients after treatment. This is largely due to CT’s high spatial resolution and its ability to perform volume imaging effectively. This review provides an up-to-date overview of the increasing role of cardiovascular CT in the management of bicuspid aortic valve (BAV). It covers various aspects, including BAV morphology, optimal sizing for transcatheter aortic valve replacement (TAVR), and post-procedural monitoring. We highlight significant innovations, such as supra-annular sizing techniques and artificial intelligence (AI)-guided analysis, that position CT at the nexus of anatomy, function, and targeted treatment. Additionally, we address controversies concerning inconsistencies in sizing algorithms, recent classification challenges, and radiation exposure. Future development areas include AI predictive tools, radiomic phenotyping, and CT-guided precision medicine. This synthesis aims to provide clinicians and researchers with a high-level guide to the clinical integration of cardiovascular CT and its future in the BAV population. This review provides the most current, comprehensive synthesis on the pivotal role of cardiovascular CT in BAV management, offering a roadmap for integrating advanced imaging into clinical practice and guiding future research priorities. Full article

As a key technology in the intelligent interpretation of remote sensing, remote sensing image change detection aims to automatically identify surface changes from images of the same area acquired at different times. Although vision foundation models have demonstrated outstanding capabilities in image feature representation, their inherent patch-based processing and global attention mechanisms limit their effectiveness in perceiving multi-scale targets. To address this, we propose a multi-scale remote sensing image change detection network based on a vision foundation model, termed SAM-MSCD. This network integrates an efficient parameter fine-tuning strategy with a cross-temporal multi-scale feature fusion mechanism, significantly improving change perception accuracy in complex scenarios. Specifically, the Low-Rank Adaptation mechanism is adopted for parameter-efficient fine-tuning of the Segment Anything Model (SAM) image encoder, adapting it for the remote sensing change detection task. A bi-temporal feature interaction module(BIM) is designed to enhance the semantic alignment and the modeling of change relationships between feature maps from different time phases. Furthermore, a change feature enhancement module (CFEM) is proposed to fuse and highlight differential information from different levels, achieving precise capture of multi-scale changes. Comprehensive experimental results on four public remote sensing change detection datasets, namely LEVIR-CD, WHU-CD, NJDS, and MSRS-CD, demonstrate that SAM-MSCD surpasses current state-of-the-art (SOTA) methods on several key evaluation metrics, including the F1-score and Intersection over Union(IoU), indicating its broad prospects for practical application. Full article

Background/Objectives: Melanoma remains one of the most malignant types of skin cancer with rising incidence numbers, despite the progress made in the prevention and management of the disease. Recent technological advancements, such as developments in the field of molecular biology, imaging, and artificial intelligence (AI), have led to a paradigm shift in the diagnosis, assessment, and management of melanoma. The current review aims to integrate current research on melanoma, moving beyond the boundaries of conventional histological analysis. Methods: This is a critical appraisal narrative review that focuses on recent studies in the areas of translation research and digital health with regard to melanoma. This research particularly targeted recent studies within the last five years, with landmark studies implicated when appropriate. Evidence was synthesized within the major categories that include epidemiology, early diagnosis, histopathology, predictive biomarkers, genetic/epigenetic changes, AI-assisted diagnostic platforms, and novel therapeutic platforms & targets. Results: Early detection techniques, innovative imaging, and biomarker-guided risk adjustment can improve diagnostic accuracy and prognostic stratification. The potential of AI in dermoscopy, digital pathology, and decision analytical systems is evident, although validation, bias, and integration issues need to be addressed. Advances in immunotherapy, targeted therapies, and novel molecular/immunological targets are expanding and facilitating integrated and personalized management. Conclusions: There is a trend in melanoma research to shift towards an integrated diagnostic platform that involves the use of AI, molecular characterization, and clinical inputs to enable more accurate and personalized diagnoses. To realize this potential, there is a need to validate, collaborate, and address ethics and implementation. Full article

Targeting the thinly bedded and strongly heterogeneous tight sandstone gas reservoirs of the Shaximiao Formation in the Yingshan area of the Sichuan Basin, this study establishes an integrated workflow that combines high-fidelity 3D seismic processing with quantitative interpretation to address key challenges such as insufficient resolution of conventional seismic data under complex near-surface conditions and difficulty in depicting sand-body geometries. On the processing side, a 2D-3D integrated amplitude-preserving high-resolution strategy is applied. In contrast to conventional workflows that treat 2D and 3D datasets independently and often sacrifice true-amplitude characteristics during static correction and noise suppression, the proposed approach unifies first-break picking and static-correction parameters across 2D and 3D data while preserving relative amplitude fidelity. Techniques such as true-surface velocity modeling, coherent-noise suppression, and wavelet compression are introduced. As a result, the effective frequency bandwidth of the newly processed data is broadened by approximately 10–16 Hz relative to the legacy dataset, and the imaging of small faults and narrow river-channel boundaries is significantly enhanced. On the interpretation side, ten sublayers within the first member of the Shaximiao Formation are correlated with high precision, yielding the identification of 41 fourth-order local structural units and 122 stratigraphic traps. Through seismic forward modeling and attribute optimization, a set of sensitive attributes suitable for thin-sandstone detection is established. These attributes enable fine-scale characterization of sand-body distributions within the shallow-water delta system, where fluvial control is pronounced, leading to the identification of 364 multi-phase superimposed channels. Based on attribute fusion, rock-physics-constrained inversion, and integrated hydrocarbon-indicator analysis, 147 favorable “sweet spots” are predicted, and six well locations are proposed. The study builds a reservoir-forming model of “deep hydrocarbon generation–upward migration, fault-controlled charging, structural trapping, and microfacies-controlled enrichment,” achieving high-fidelity imaging and quantitative prediction of tight sandstone reservoirs in the Shaximiao Formation. The results provide robust technical support for favorable-zone evaluation and subsequent exploration deployment in the Yingshan area. Full article

Multi-platform aerial remote sensing supports critical applications including wide-area surveillance, traffic monitoring, maritime security, and search and rescue. However, constrained by observation altitude and sensor resolution, targets inherently exhibit small-scale characteristics, making small object detection a fundamental bottleneck. Aerial remote sensing faces three unique challenges: (1) spatial heterogeneity of modality reliability due to scene diversity and illumination dynamics; (2) conflict between precise localization requirements and progressive spatial information degradation; (3) annotation ambiguity from imaging physics conflicting with IoU-based training. This paper proposes RAPT-Net with three core modules: MRAAF achieves scene-adaptive modality integration through two-stage progressive fusion; CMFE-SRP employs hierarchy-specific processing to balance spatial details and semantic enhancement; DS-STD increases positive sample coverage to 4× through spatial tolerance expansion. Experiments on VEDAI (satellite) and RGBT-Tiny (UAV) demonstrate mAP values of 62.22% and 18.52%, improving over the state of the art by 4.3% and 10.3%, with a 17.3% improvement on extremely tiny targets. Full article

With rapid urbanization, urban renewal has become increasingly important. Traditional research has relied on expert assessments and objective indicators, lacking scalable frameworks that effectively translate street-level conditions into actionable renewal strategies. This study proposes a Vision–Language Model (VLM)-based framework to address these gaps, using the Hongshan Central District of Urumqi, China, as a case study. Specifically, we collected 4215 street-view images (SVIs) and employed VLMs to assess six perceptual dimensions (i.e., safety, liveliness, beauty, wealthiness, depressiveness, and boringness), together with textual descriptions. The best-performing model, selected by a 500-respondent perception survey validation, was used to conduct spatial pattern and text mining analyses to inform targeted urban renewal strategies. Results show that (1) VLMs have a high consistency with humans in evaluating the spatial perception of six dimensions; (2) spatial clustering analysis successfully delineated four distinct renewal priority tiers, confirming the method’s capability in translating perceptual data into actionable spatial strategies; and (3) textual mining of the VLM’s rationales revealed that areas with lower perceptual scores are predominantly characterized by deficiencies in foundational infrastructure and street-level order, thereby providing explanatory evidence directly linked to the generated renewal priorities. This study provides a generative artificial intelligence (GAI)-driven and interpretable evaluation framework for urban renewal decision-making, facilitating precision-oriented and intelligent urban regeneration. Full article

Land cover mapping is a crucial component of the Copernicus Land Monitoring Service, but existing products underestimate urbanized areas and small-scale man-made objects, limiting their ability to capture the complexity of built environments. Long-baseline monostatic–bistatic Synthetic Aperture Radar (SAR) images, such as the ones that will be made available by the upcoming PLATiNO-1 mission, have the potential to contribute to the detection of the mentioned targets, e.g., by traditional supervised classification approaches. Since bistatic measurements from the PLATiNO-1 mission are not yet available, repeat-pass COSMO-SkyMed second generation (CSG) images collected with different incidence angles are employed to emulate the expected diversity of future monostatic–bistatic products. A complete classification pipeline is developed, and a structured dataset of 48 features is built, combining intensity, polarimetric, spatial, and textural descriptors to train an XGBoost model to identify urban targets within a representative area in Italy. The results demonstrate stable performance, with F1 scores around 0.73 and true positive rates close to 80%, showing good agreement with reference data and confirming the feasibility of the proposed methodology. Although conceived as a proof of concept, the study shows that integrating multi-angle information into classification tasks can improve the detection of man-made structures and provide an additional information layer to be integrated with Copernicus services. Full article

Understanding how tacit knowledge embedded in visual materials is accessed and utilized during evaluation tasks remains a key challenge in human–computer interaction and visual expertise research. Although eye-tracking studies have identified systematic differences between experts and novices, findings remain inconsistent, particularly in art-related visual evaluation contexts. This study examines whether tacit aspects of visual evaluation can be inferred from gaze behavior by comparing individuals with and without formal art education. Visual evaluation was assessed using a structured, prompt-based task in which participants inspected artistic images and responded to items targeting specific visual elements. Eye movements were recorded using a screen-based eye-tracking system. Areas of Interest (AOIs) corresponding to correct-answer regions were defined a priori based on expert judgment and item prompts. Both AOI-level metrics (e.g., fixation count, mean, and total visit and gaze durations) and image-level metrics (e.g., fixation count, saccade count, and pupil size) were analyzed using appropriate parametric and non-parametric statistical tests. The results showed that participants with an art-education background produced more fixations within AOIs, exhibited longer mean and total AOI visit and gaze durations, and demonstrated lower saccade counts than participants without art education. These patterns indicate more systematic and goal-directed gaze behavior during visual evaluation, suggesting that formal art education may shape tacit visual evaluation strategies. The findings also highlight the potential of eye tracking as a methodological tool for studying expertise-related differences in visual evaluation processes. Full article

Potential tropospheric noise is a critical factor that undermines the effectiveness of deformation monitoring in Synthetic Aperture Radar Interferometry (InSAR) technologies. In most scenarios, many point targets within the InSAR deformation monitoring area either do not undergo deformation or exhibit only minimal deformation trends. The phases of densely distributed stable points can effectively respond to spatial tropospheric delays, particularly turbulent atmospheric phases. This study proposes a data-driven InSAR atmospheric correction method by exploring how to use these densely stable InSAR time series to model atmospheric phase delays. Our focus is on selecting stable InSAR time series point targets and evaluating the impact of different densities of stable points on atmospheric correction performance. Analysis of 645 interferograms derived from 217 Sentinel-1A SAR images, spanning from 13 June 2017 to 15 November 2024, demonstrates that the proposed method reduces the Root Mean Square Error (RMSE) by 70%, 59%, and 69% compared to the terrain-related linear approach, the General Atmospheric Correction Online Service, and common scene stacking methods, respectively. In addition, simulation data and leveling data were used to validate the proposed method. This article does not develop an independent InSAR atmospheric correction method. Instead, the proposed approach starts with the InSAR deformation time series, allowing for easy integration into existing InSAR workflows and widely used atmospheric correction strategies. It can serve as a post-processing tool to improve InSAR time series analysis. Full article

Visual cues in urban street environments shape residents’ perceived safety, and these perceptions often differ across social groups. Using Shanghai as a case study, this research focuses on two vulnerable populations: older adults and migrants. In the context of rapid urban transformation and increasingly fine-grained governance, perceived safety not only reflects environmental experience but also relates to whether different social groups can receive equitable perceptual support and access to opportunities for public-space use. We trained a deep learning model and rated perceived safety using over 160,000 street-level images, integrated with demographic census data at the neighborhood level, to systematically examine inequalities in visual environment perception and underlying group-specific mechanisms. However, existing studies have largely relied on small-sample surveys or average-effect analyses, and systematic evidence remains limited that can simultaneously characterize city-scale inequalities in perceived safety, disparities in group exposure, and group-specific mechanisms, while translating findings into actionable guidance for targeted governance. Firstly, we quantified spatial inequality in perceived safety using the Gini coefficient and the Theil T index. Decomposition results indicate that the remaining disparity is primarily associated with between-group differences linked to social structure. Nonparametric tests and multiple linear regression further identified significant interactions between demographic characteristics (the share of older adults and the migrant proportion) and visual environmental features, confirming group-differentiated responses to comparable streetscape conditions. In addition, we developed a priority governance index that combines perceived safety scores with vulnerability indicators to spatially identify neighborhoods requiring targeted interventions. Results suggest relatively low overall spatial inequality in perceived safety at the city scale, while decomposition analyses reveal clear group-structured disparities between central and peripheral areas and between local residents and migrants. Migrants are more frequently concentrated in neighborhoods with lower perceived safety. Priority intervention areas are primarily older-adult communities in central districts and migrant settlements in peripheral areas, characterized by lower perceived safety and higher demographic vulnerability. These findings underscore the need to shift urban renewal from uniform improvements toward differentiated strategies that account for perceptual equity and social identity. Our main contribution is not the development of a new network architecture but the alignment of image-based perception estimates with demographic vulnerability at the neighborhood scale. By combining inequality decomposition with tests of interaction mechanisms, we provide governance-relevant evidence for identifying priority intervention areas and advancing fine-grained renewal decisions oriented toward visual justice. Full article

In recent years, real-time monitoring of broiler chicken weight has become crucial for assessing growth and health status. Currently, obtaining weight data often relies on manual collection. However, this process is cumbersome, labor-intensive, and inefficient. This paper proposes a broiler carcass weight detection model based on deep learning image segmentation and regression to address these issues. The model first segments broiler carcasses and then uses the pixel area of the segmented region as a key feature for a regression model to predict weight. A custom dataset comprising 2709 images from 301 Taihu yellow chickens was established for this study. A novel segmentation network, AR2U-AtNet, derived from R2U-Net, is proposed. To mitigate the interference of background color and texture on target carcasses in slaughterhouse production lines, the Convolutional Block Attention Module (CBAM) is introduced to enable the network to focus on areas containing carcasses. Furthermore, broilers exhibit significant variations in size, morphology, and posture, which impose high demands on the model’s scale adaptability. Selective Kernel Attention (SKAttention) is therefore integrated to flexibly handle broiler images with diverse body conditions. The model achieved an average Intersection over Union (mIoU) score of 90.45%, and Dice and F1 scores of 95.18%. The regression-based weight prediction achieved an $R^2$ value of 0.9324. The results demonstrate that the proposed method can quickly and accurately determine individual broiler carcass weights, thereby alleviating the burden of traditional weighing methods and ultimately improving the production efficiency of yellow-feather broilers. Full article

The Venice lagoon is the largest in the Mediterranean Sea. The historic city of Venice, located on a cluster of islands in the centre of this lagoon, is an enchanting and iconic destination for national and international tourists. The historical centre of Venice and the other islands of the lagoon, such as Burano, Murano and Torcello, attract crowds of tourists every year. Transportation is provided by boats navigating the lagoon along a network of canals. The lagoon itself attracts visitors who enjoy various outdoor recreational activities in the open air, such as fishing and sunbathing. While statistics are available for the activities targeting the islands, no information is currently available on the spatio-temporal distribution of recreational activities across the lagoon waters. This study explores the feasibility of using Sentinel-2 satellite images to assess and map the spatio-temporal distribution of boats in the Venice Lagoon. Cloud-free Level-2A images have been selected to study seasonal (summer vs. winter) and weekly (weekends vs. weekdays) variabilities in 2023, 2024, and 2025. The RGB threshold filtering and the U-Net Semantic Segmentation were applied to the Sentinel-2 images to ensure reliable results. Two spatial indices were produced: (i) a Water Recreation Index (WRI), identifying standing boats in areas attractive for recreation; and (ii) a Water Transportation Index (WTI), mapping moving boats along the canals. Multi-temporal WRI maps allow areas with recurring recreational activities—that are significantly higher in the summer compared to winter, and on weekends compared to other weekdays—to be identified. The WTI identifies canal paths with higher traffic intensity with seasonal and weekly variations. The latter should be targeted by measures for traffic control to limit wave induced erosion, while the first could be subject to protection or development strategies. Full article

A femtosecond laser serves as an excellent tool for efficiently fabricating large-area anti-reflective microhole arrays on infrared windows. The impact of the arrangement of the microholes during processing on final performance, however, remains unclear. Here, microhole arrays were fabricated on MgF₂ windows using a femtosecond laser. The optical performance was analyzed by the finite-difference time-domain method, focusing on the effects of in-plane arrangement deviation and double-sided alignment error. Simulation results indicate that the arrangement variations alter the average transmittance by less than 0.02%. Analysis via effective medium theory revealed that, within the target band, the microstructure array collectively functions as a thin film with a gradient refractive index. Its macroscopic properties show little sensitivity to minor misalignments at the microscopic scale. As a proof of concept, a large-area (20 mm × 20 mm) double-sided antireflection window was rapidly fabricated by employing a zigzag scanning strategy, which achieved an average transmittance exceeding 97.5% and exhibited a high degree of consistency between the simulated and experimental results. Upon final integration into the infrared thermal imaging system, this window not only enhanced the richness of detail in captured images but also improved target contrast. Full article

Introduction: Massage therapy delivers structured mechanosensory input that can influence brain function, yet the central mechanisms and potential for neuroplastic change have not been synthesized across neuroimaging modalities. This mechanistic review integrates evidence from electroencephalography (EEG), functional MRI (fMRI), and functional near-infrared spectroscopy (fNIRS) to map how massage alters human brain activity acutely and over time and to identify signals of longitudinal adaptation. Materials and Methods: We conducted a scoping, mechanistic review informed by PRISMA/PRISMA-ScR principles. PubMed/MEDLINE, Cochrane Library, Google Scholar, and ResearchGate were queried for English-language human trials (January 1990–July 2025) that (1) delivered a practitioner-applied manual massage (e.g., Swedish, Thai, shiatsu, tuina, reflexology, myofascial techniques) and (2) measured brain activity with EEG, fMRI, or fNIRS pre/post or between groups. Non-manual stimulation, structural-only imaging, protocols, and non-English reports were excluded. Two reviewers independently screened and extracted study, intervention, and neuroimaging details; heterogeneity precluded meta-analysis, so results were narratively synthesized by modality and linked to putative mechanisms and longitudinal effects. Results: Forty-seven studies met the criteria: 30 EEG, 12 fMRI, and 5 fNIRS. Results: Regarding EEG, massage commonly increased alpha across single sessions with reductions in beta/gamma, alongside pressure-dependent autonomic shifts; moderate pressure favored a parasympathetic/relaxation profile. Connectivity effects were state- and modality-specific (e.g., reduced inter-occipital alpha coherence after facial massage, preserved or reorganized coupling with hands-on vs. mechanical delivery). Frontal alpha asymmetry frequently shifted leftward (approach/positive affect). Pain cohorts showed decreased cortical entropy and a shift toward slower rhythms, which tracked analgesia. Somatotopy emerged during unilateral treatments (contralateral central beta suppression). Adjuncts (e.g., binaural beats) enhanced anti-fatigue indices. Longitudinally, repeated programs showed attenuation of acute EEG/cortisol responses yet improvements in stress and performance; in one program, BDNF increased across weeks. In preterm infants, twice-daily massage accelerated EEG maturation (higher alpha/beta, lower delta) in a dose-responsive fashion; the EEG background was more continuous. In fMRI studies, in-scanner touch and reflexology engaged the insula, anterior cingulate, striatum, and periaqueductal gray; somatotopic specificity was observed for mapped foot areas. Resting-state studies in chronic pain reported normalization of regional homogeneity and/or connectivity within default-mode and salience/interoceptive networks after multi-session tuina or osteopathic interventions, paralleling symptom improvement; some task-based effects persisted at delayed follow-up. fNIRS studies generally showed increased prefrontal oxygenation during/after massage; in motor-impaired cohorts, acupressure/massage enhanced lateralized sensorimotor activation, consistent with use-dependent plasticity. Some reports paired hemodynamic changes with oxytocin and autonomic markers. Conclusions: Across modalities, massage reliably modulates central activity acutely and shows convergent signals of neuroplastic adaptation with repeated dosing and in developmental windows. Evidence supports (i) rapid induction of relaxed/analgesic states (alpha increases, network rebalancing) and (ii) longer-horizon changes—network normalization in chronic pain, EEG maturation in preterm infants, and neurotrophic up-shifts—consistent with trait-level recalibration of stress, interoception, and pain circuits. These findings justify integrating massage into rehabilitation, pain management, mental health, and neonatal care and motivate larger, standardized, multimodal longitudinal trials to define dose–response relationships, durability, and mechanistic mediators (e.g., connectivity targets, neuropeptides). Full article

UAV-based object detection faces critical challenges including extreme scale variations (targets occupy 0.1–2% image area), bird’s-eye view complexities, and all-weather operational demands. Single RGB sensors degrade under poor illumination while infrared sensors lack spatial details. We propose ATM-Net, a lightweight multimodal RGB–infrared fusion network for robust UAV vehicle detection. ATM-Net integrates three innovations: (1) Asymmetric Recurrent Fusion Module (ARFM) performs “extraction→fusion→separation” cycles across pyramid levels, balancing cross-modal collaboration and modality independence. (2) Tri-Dimensional Attention (TDA) recalibrates features through orthogonal Channel-Width, Height-Channel, and Height-Width branches, enabling comprehensive multi-dimensional feature enhancement. (3) Multi-scale Adaptive Feature Pyramid Network (MAFPN) constructs enhanced representations via bidirectional flow and multi-path aggregation. Experiments on VEDAI and DroneVehicle datasets demonstrate superior performance—92.4% mAP50 and 64.7% mAP50-95 on VEDAI, 83.7% mAP on DroneVehicle—with only 4.83M parameters. ATM-Net achieves optimal accuracy–efficiency balance for resource-constrained UAV edge platforms. Full article