Search Results (250)

Nucleic acids exhibit structured electromagnetic features shaped by classical electronegativity (EN) patterns. Mapping Pauling EN values across DNA and RNA reveals a largely invariant, high-EN phosphodiester backbone that provides a consistent electrostatic scaffold, while nucleobases introduce sequence-specific electron density shifts that generate tunable recognition fields. Together, these features create a dual-system framework in which a stable electrostatic background supports sequence-dependent informational cues. Within this environment, short-timescale vibronic interactions may arise from patterned vibrational and electronic behavior, producing modest “quantum overlay” effects compatible with known decoherence constraints. These structured, anisotropic electrostatic features may help explain differences in stability between DNA and RNA, the functional outcomes of nucleoside modifications such as N¹-methylpseudouridine (m¹Ψ), and the sensitivity of translational fidelity to small architectural perturbations. The framework yields experimentally testable predictions involving vibrational relaxation, dipole reorientation, and charge-transfer behavior, offering a classical-to-quantum interpretive bridge that may inform the design of next-generation therapeutic mRNAs. Full article

Colorectal cancer (CRC) remains the third most common malignancy worldwide and a leading cause of cancer mortality, largely driven by metastatic dissemination. Among metastatic routes, lymphatic spread is crucial to determine the prognosis and establish an adequate therapeutic strategy. Lymph node metastasis (LNM) defines stage III disease in the TNM classification, guiding adjuvant chemotherapy and surgical planning. However, nodal staging based on lymphadenectomy and histopathology is invasive, time-consuming, and may lead to overtreatment. Conventional imaging modalities, including computed tomography, magnetic resonance imaging, and endorectal ultrasound, show limited sensitivity and specificity for small or micro-metastatic nodes. Despite multimodal progress, no non-invasive technique reliably identifies malignant nodes in real time. PET–MRI, contrast-enhanced ultrasound, photoacoustic and fluorescence approaches, ICG mapping, and sentinel node biopsy improve detection but remain limited by specificity, cost, or availability. Extranodal extension (ENE) and tumor deposits (TDs) carry major prognostic value, reflecting aggressive biology and association with distant spread. Meanwhile, phylogenetic studies challenge linear dissemination models, indicating that some metastases arise directly from the primary tumor or TDs rather than LNMs. These data support refinement of staging and surgical strategies according to tumor biology rather than purely anatomical criteria. High-frequency quantitative ultrasound (HF-QUS) enables real-time, operator-independent, three-dimensional nodal assessment with reported sensitivity and specificity exceeding 85%. Combined with artificial intelligence and molecular profiling, it may support biologically informed staging, reduce unnecessary surgery, and foster precision oncology. Lymphatic dissemination in CRC offers a platform to merge tumor biology with technological innovation, where advanced imaging, molecular insight, and artificial intelligence may redefine nodal staging toward precision, non-invasive care. Full article

GPS-derived floating car data (FCD) provide spatially continuous urban traffic observations without fixed-sensor infrastructure. This study develops a spatiotemporal baseline framework jointly modelling day type and precipitation for 1189 junction-level nodes in Budapest. A six-phase pipeline—GPS preprocessing, coordinate reprojection, FME (Feature Manipulation Engine, Safe Software Inc., Surrey, BC, Canada)-based map-matching, junction-level aggregation, Voronoi meteorological allocation, and dataset assembly—was applied to 44.1 million 10 s records from approximately 1100 probe vehicles (November 2024–December 2025). Public holidays form a structurally distinct traffic flow pattern compared to Sundays (r = 0.71) and to regular workdays (r = 0.42); morning peak shifts to 09:00–11:00 and pooling holidays with Sundays introduces reference errors of 15–25%. Precipitation raises morning peak volumes by 6–17% across all zones while afternoon peaks remain statistically unchanged, consistent with commuter inertia; Saturday volumes fall by 7–15%. Rainy Wednesdays reach 109–112% of the Monday dry reference in inner zones, attributed to hybrid workers advancing their office day. Pairwise junction correlations show a non-monotonic distance-decay pattern, and time-lagged cross-correlation identifies 23 anticipative junction pairs with 60–90 min lead times. The results could potentially help decision making when developing city-wide infrastructure and tuning traffic signals so that traffic can be optimised and adapt to both real-time natural and social effects. The resulting baselines map onto DATEX II (Data Exchange standard, CEN EN 16157) ElaboratedDataPublication fields, supporting metadata publication on the Hungarian National Access Point under EU Regulation 2022/670/EU. Full article

Cybersecurity conformity assessment is increasingly shaped by the Radio Equipment Directive (RED) delegated act, the EN 18031 harmonized standards, the Cyber Resilience Act, and industrial standards such as International Electrotechnical Commission (IEC) 62443. ISO/IEC 17025:2017 provides a general laboratory competence framework, but its application to qualitative cybersecurity testing, rapidly changing toolchains, and automation-assisted evidence workflows remains under-specified. This paper proposes a digitalized quality-management framework that translates ISO/IEC 17025 clauses into cybersecurity-native controls for scope definition, method governance, toolchain control, evidence traceability, decision rules, technical review, and corrective-action feedback. An accreditation-style single-laboratory case study integrates a European Telecommunications Standards Institute (ETSI) TS 103 701 assessment workbook, an IEC 62443 corrective-action dataset, ISO/IEC 17025 internal audit findings, and laboratory governance records. In the ETSI workbook, the Conformity Statement Ambiguity Index (CSAI) decreases from 0.976 in the draft state to 0.050 after review, with 37 previously inconclusive provisions moving to PASS. This result is interpreted as improved determinability within the assessed workflow, not as cross-laboratory validation. The study contributes a clause-to-workflow operationalization of ISO/IEC 17025, an analytic design for heterogeneous assurance artefacts, and an EN 18031 evidence-mapping approach for Radio Equipment Directive readiness. Full article

Background: The presence of left atrial fibrosis is a marker of advanced remodeling and is associated with a worse outcome after pulmonary vein isolation (PVI). Conventional fluoroscopy-only cryoballoon ablation (CBA) lacks this prognostic information. The addition of electroanatomical mapping (EAM) using the inner lumen spiral catheter allows accurate voltage assessment of the left atrial posterior wall. However, the value of the finding of posterior wall low-voltage zones (pwLVZs) is unknown. Purpose: To study the value of left atrial voltage maps during CBA by comparing clinical and procedural characteristics and clinical outcome between patients with and without pwLVZs. Methods: A cohort of 250 consecutive patients who underwent index CBA for atrial fibrillation was analyzed. All patients underwent pre- and post-procedural EAM using the Achieve^TM catheter and EnSite^TM mapping system. The presence of LVZs was evaluated at the postprocedural voltage map of the posterior wall. Clinical success was defined as freedom from documented AF or atrial tachycardia (AT) >30 s after 1 year. Results: PwLVZs were found in 41/250 (16.4%) of patients. Patients with pwLVZs were older (69.3 ± 8.5 vs. 64.2 ± 10.4; p = 0.003), more frequently female (63.4% vs. 32.5%; p < 0.001) and had higher CHA₂DS₂-VASc scores (3.0 ± 1.6 vs. 2.0 ± 1.5; p < 0.001). The incidence of obesity (31.7% vs. 25.8%; p = 0.048), structural heart disease (35.5% vs. 17.4%; p = 0.021) and persistent AF (68.3% vs. 43.8%; p = 0.004) was higher in the pwLVZs group. Kaplan–Meier analysis of clinical outcome showed a higher recurrence rate in the pwLVZs group. The finding of pwLVZs was a predictor of atrial arrhythmia recurrence during follow-up (HR 2.583; 95%CI: 1.334–5.002; p = 0.005). Conclusions: In CBA facilitated by integrated EAM, pwLVZ was associated with older age, female sex, higher CHADS-VASc scores, obesity, structural heart disease and persistent AF. The finding of pwLVZs is predictive of a worse clinical outcome. Full article

Methane source-rate inversion from remote-sensing plume imagery is essential for emissions monitoring, but its accuracy is often limited by uncertainty in ancillary wind information. This study examines how physical knowledge can be integrated into a deep-learning inversion model when the available wind input is imperfect. Using a controlled large-eddy-simulation (LES) benchmark designed for EnMAP/PRISMA-style imaging-spectrometer methane quantification, we compare six models that span image-only regression, flexible wind conditioning, simplified hard integrated-mass-enhancement (IME) coupling, and soft physics-guided learning under clean inputs, deterministic wind bias, stochastic Gaussian wind noise, and source-rate-stratified tests. Under clean benchmark conditions, flexible wind conditioning provides the best scalar accuracy, with FiLM reaching a mean absolute percentage error (MAPE) of 6.19% and a root mean squared error (RMSE) of 1323.36, followed closely by Concat (MAPE 6.37%, RMSE 1325.69). The simplified hard-coupling model is sensitive to wind perturbations: DIN-hard rises from MAPE 8.44% under clean inputs to 31.39% and 26.89% under deterministic wind-bias multipliers α = 0.7 and α = 1.3, respectively, and becomes unstable under stronger Gaussian wind noise in the tested protocol. By contrast, DIN-soft-v2 remains competitive under clean conditions (MAPE 6.39%, RMSE 1360.94), follows smoother degradation under biased or noisy wind, and improves plume spatial diagnostics relative to DIN-soft (center-of-mass shift 3.92 versus 4.07 pixels; plume alignment degree 2.60 versus 2.72 degrees). The calibrated IME-style physical baseline reaches a clean MAPE 24.45%, indicating that the learning-based models substantially outperform this benchmark physical proxy. Within this LES-based benchmark and the tested wind-perturbation protocols, the results suggest that IME-inspired physical knowledge is more robustly incorporated as a calibratable soft prior than as the simplified hard log-additive forward coupling considered here; however, transfer to real satellite scenes still requires validation. Full article

Optical coherence tomography angiography (OCTA) is a depth-resolved, label-free optical imaging modality that uses motion contrast from repeated B-scans to reconstruct retinal microvasculature and provide co-registered en face views of the superficial and deep vascular plexuses (SVP and DVP). This capability is valuable for early diabetic retinopathy (DR) assessment, where deep-plexus perfusion deficits may precede clinically evident disease. However, microvascular differences among healthy controls, diabetic eyes without clinically apparent retinopathy, and mild DR are subtle and unevenly distributed across the two vascular slabs, while most deep learning methods prematurely fuse the plexuses and weaken depth-specific evidence provided by OCTA. To address this, we propose Class-Path Specific Representation Distillation and Reasoning (CPS-RDR), an interpretable framework that aligns model reasoning with the layered organization of OCTA. A frozen DINOv2-initialized dual-branch Vision Transformer preserves separate SVP and DVP representations, while class- and path-conditioned diagnostic queries instantiate four reasoning pathways for layer-specific evidence extraction and directional cross-plexus interaction. A lightweight EvidenceFusion head linearly integrates pathway-wise evidence, enabling final predictions to be decomposed into pathway-specific contributions. On 99 eyes from 55 participants, CPS-RDR achieved 97.29% accuracy, 0.9932 macro-AUC, and 0.9829 macro-F1 under five-fold patient-level cross-validation, outperforming seven representative baselines, while producing path-resolved maps that reveal how superficial- and deep-layer optical signals jointly support early DR stratification. Full article

Digital Surface Model (DSM) products play an indispensable role in mapping production and geospatial applications. Their importance in hyperspectral topographic correction is particularly critical, as they determine key terrain parameters such as slope, aspect, and solar incidence angle. Therefore, it is essential to evaluate the quality of DSM products. To address this issue, this study proposes a novel framework to indirectly assess DSM quality from the perspective of radiometric consistency after atmospheric and topographic correction. Two DSM datasets, the Copernicus DEM (GLO-30) and the Chinese ZY-3 DSM, are integrated into a hyperspectral correction workflow using EnMAP and ZY-1 02D data over two study areas. Multiple evaluation metrics, including visual assessment, regression analysis, interquartile range (IQR), and relative difference in mean reflectance (RDMR), are employed to quantify spectral variability and radiometric stability. The results show that both Copernicus DEM and ZY-3 DSM achieve comparable performance in topographic correction, while the Copernicus DEM shows slightly better consistency across most evaluation metrics. These findings demonstrate that topographic correction outcomes inherently contain DSM quality information and that spectral consistency metrics can serve as a valuable complementary tool for DSM evaluation, particularly in the absence of ground truth data. Full article

Given the increasing frequency, severity, and socioecological impacts of wildfires, there is an urgent need for robust frameworks to better characterize fire behavior and flammability patterns across ecosystems to support early warning, mitigation, and management strategies. However, flammability remains difficult to quantify and scale, as it involves multiple interacting components that are typically measured at the bench scale. This study aimed to establish empirical links between spectral information, plant traits, and flammability metrics, and to scale these relationships to satellite imagery to translate these metrics into a spatial context. We combined laboratory spectroscopy, plant trait measurements including leaf mass per area, carbon, and cellulose, and combustion experiments using a simple and reproducible burning device. In total, 84 samples were collected and analysed, allowing us to characterise how spectral signatures relate to vegetation traits and fire behaviour. Spectral indices were developed to estimate plant traits, which were subsequently used as predictors in flammability models. These models were then transferred to Environmental Mapping and Analysis Program (EnMAP) hyperspectral imagery to derive spatial estimates across eucalypt forests and grasslands of the Australian Capital Territory (ACT). Spectral information distinguished fuel types and captured variability of the plant traits, while these traits showed associations with combustion behaviour. Based on these links, the best-performing model predicted the rate of temperature increase, a combustibility metric, in eucalypt forests (R² = 0.70; Root Mean Square Error = 32.48 °C/s). In contrast, grassland models showed limited predictive performance, likely due to weaker relationships between plant traits and flammability metrics. Overall, this study demonstrates a practical and scalable approach for deriving flammability maps from hyperspectral and in situ data, highlighting the potential of plant-trait-based remote sensing. The resulting maps should not be interpreted as standalone fire risk products, but rather as a characterization of the structural and biochemical drivers of flammability. The main constraint of this work is the limited sample size. Future research should expand spatial and temporal coverage to better capture vegetation variability and enable the inclusion of independent validation datasets. Exploring alternative combustion protocols and testing more advanced spectral modelling approaches for trait estimation would provide additional insights. Full article

Background/Objectives: Lung cancer is one of the most common and lethal malignancies worldwide. Early detection remains challenging due to its variable biological behavior. Computed tomography (CT) is the primary imaging method used for early detection. However, the manual interpretation of CT scans is constrained by several challenges such as reliance on expert experience, increasing clinical workload, and considerable variability among observers. Methods: This study introduces an explainable transformer-based framework capable of distinguishing among the three principal clinical categories of lung cancer (small-cell lung cancer, non-small-cell lung cancer, and normal) while simultaneously generating automated radiology reports from CT images. In contrast to conventional single-slice methodologies, the proposed model employs a multi-slice volumetric encoding strategy that captures spatial continuity and anatomical relationships across the CT slices. Visual features extracted by a ViT-based encoder are transformed into a compact patient-level representation through a Learnable Query Attention Pooling (LQAP) mechanism, and this unified representation is subsequently used for both three-class prediction and report generation with a GPT-2-based decoder. To enhance explainability, slice-wise Grad-CAM maps are produced, visually highlighting the anatomical cues that guide the model’s decisions. Results: Experiments conducted on the newly curated LungCA dataset comprising 767 patients demonstrate that the model achieves 97.40% accuracy in the Turkish (TR) reporting scenario and 94.81% accuracy in the English (EN) scenario, alongside strong alignment with human-written reports in BLEU, ROUGE, METEOR, and CIDEr metrics. Conclusions: The findings demonstrate that the proposed multi-slice transformer framework achieves robust performance in both classification and radiology report generation, enhances transparency throughout the decision-making process, and provides a robust artificial intelligence solution capable of effectively supporting clinical workflows in lung cancer assessment. Full article

HyperCoreg is an automated, end-to-end pipeline for geometric co-registration of spaceborne hyperspectral imagery (PRISMA L2D and EnMAP L2A) to Sentinel-2 Level-2A reference data. The workflow addresses scene-dependent geolocation errors that hinder reliable data fusion and multi-temporal analyses, particularly in cloud-affected acquisitions. HyperCoreg builds on the AROSICS framework without replacing its image-matching engine and extends it at the workflow level through four operational functions: automated Sentinel-2 candidate selection, hyperspectral-to-multispectral band pairing, sequential alignment logic, and quality-controlled acceptance. The main output is a co-registered hyperspectral cube along with comprehensive metrics, per-scene reports, and optional diagnostic products that support accessible quality control. Performance is evaluated on a long time series of PRISMA images collected from 2019 to 2025 and an EnMAP test set acquired in 2025, over the Metropolitan City of Rome (Italy). The multi-sensor dataset encompasses heterogeneous acquisition conditions, including variable cloud cover, illumination, and seasonal variability. The results show systematic reductions in mean residual error compared with a controlled basic AROSICS-based pipeline configuration. The largest gains are achieved in challenging conditions where tie points are sparse or unevenly distributed. By improving geometric consistency, this pipeline facilitates spatial layering and integration of hyperspectral data with higher-resolution urban layers and supports a range of downstream applications where data integration and spatiotemporal consistency are cornerstones of further analysis. Full article

The preparation for next-generation Earth Observation missions, such as the European Space Agency’s (ESA) Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) and Land Surface Temperature Monitoring (LSTM), requires robust pre-launch proxy datasets. Because current simulation methodologies frequently rely on isolated, platform-specific approaches, this study proposes a comprehensive, unified multisensor framework capable of dynamically generating operationally realistic CHIME and LSTM datasets from diverse airborne and satellite sources. Three distinct processing pipelines were established. For hyperspectral data simulation, precursor satellite imagery (PRISMA and EnMAP) and high-resolution airborne measurements (HySpex) were harmonized to CHIME’s 30 m specifications utilizing Spectral Response Function (SRF) adjustments, Point Spread Function (PSF) spatial resampling, and 6S atmospheric radiative transfer modeling. For thermal data simulation, archive Landsat 8/9 and ASTER imagery were transformed into LSTM’s target 50 m, 5-band configuration using a synergistic two-step approach: a physics-based Spectral Super-Resolution (SSR) module followed by an AI-driven Spatial Super-Resolution (SpSR) transformer network. Evaluated across highly diverse inland, coastal, and riverine testbeds in Italy, the simulated products demonstrated high spectral, spatial, and radiometric fidelity. While inherently constrained by the native spectral ranges of the input sensors and by the current lack of absolute on-orbit mission data for validation, the downscaled images closely reproduced complex thermal patterns and water-quality gradients. Ultimately, this scalable framework provides the remote sensing community with early access to representative datasets and mission performance assessments, while accelerating pre-launch algorithm development and testing for environmental monitoring applications—particularly those focused on water discharges. Full article

Background: Three-dimensional (3D) mapping of the left atrium (LA) using multipolar high-density (HD) catheters plays a central role in contemporary LA ablation procedures, as accurate and efficient acquisition of anatomical and electrophysiological information is essential. This study benchmarks workflow efficiency during acquisition of a predefined complete HD LA map across four widely used multipolar HD catheter designs. The analysis focuses on efficiency metrics and does not aim to assess mapping quality, arrhythmia interpretation accuracy, or clinical outcomes. Methods: We analyzed 182 consecutive patients from an ongoing cohort undergoing LA procedures, including pulmonary vein isolation and complex LA ablations, using 3D mapping in accordance with current guideline recommendations. Four multipolar HD catheters were applied according to the respective 3D mapping systems: a basket catheter (Orion, Rhythmia), a grid catheter (HD Grid, EnSite X), a penta-spline catheter (PentaRay, Carto 3), and an octa-spline catheter (OctaRay, Carto 3). For each procedure, the time required for acquisition of a complete 3D LA map and the number of acquired points were systematically recorded. LA HD mapping speed was calculated by relating LA volume to the time required for complete map acquisition. Results: The study population had a mean age of 69 years, with a median CHA₂DS₂-VASc score of 3, indicating a cohort with a moderate thromboembolic risk profile. The median LA volume index (LAVI) was 34 mL/m². Patients were distributed across four HD catheter groups, comprising 44 patients in the basket group, 29 in the grid group, 23 in the penta-spline group, and 86 in the octa-spline group. LA mapping speed differed significantly among the groups, with values of 3 mL/min in the basket group, 2.5 mL/min in the grid group, 3.1 mL/min in the penta-spline group, and the highest mapping speed observed in the octa-spline group at 5.9 mL/min. Conclusions: The octa-spline catheter was associated with a significantly higher LA mapping speed compared with other widely used HD catheters. Full article

Deep learning-based crop mapping from hyperspectral satellite data offers immense potential for capturing subtle phenological differences, yet leveraging sparse time series remains a major methodological challenge. This study evaluates the ability of the EnMAP sensor to identify nine major crop types in the intensive agricultural landscape of Southeastern Hungary. We utilized a limited time series (November, March, August) to benchmark two modeling strategies: a single-date dual-stream spatial–spectral 2D-CNN (DSS-2D) and a multi-temporal 3D-SE-ResNet. Model performance was assessed using parcel-level spatial cross-validation to ensure realistic accuracy estimates and reduce spatial autocorrelation bias. The results demonstrate that the DSS-2D model achieved superior single-date accuracy (OA > 97%), significantly outperforming pixel-based baselines. Furthermore, the multi-temporal 3D-SE-ResNet achieved a robust seasonal accuracy of 92.9%, effectively compensating for temporal sparsity by exploiting the deep spectral information of the SWIR domain. This study confirms that treating hyperspectral data as a 3D volume enables the extraction of phenological traits even from limited observations. These findings provide a strong proof-of-concept for the operational feasibility of future missions such as Copernicus CHIME for continental-scale food security monitoring. Full article

Notch wear in austenitic stainless steel turning develops rapidly and remains a key productivity limitation with carbide tools. This work identifies the initiation mechanism of notch wear when turning EN 1.4307 stainless steel using CVD-coated cemented carbide inserts with an Al₂O₃ top layer. Turning tests were performed under dry conditions, followed by optical wear measurements and chip surface analysis. The tool–chip interface chemistry and material transfer were characterized using SEM/EDS, while high-frequency acoustic emissions were recorded to resolve the dynamics of adhesive events. Thermo-mechanical FEM simulations were conducted to map contact pressure and temperature along the cutting edge. The results show that adhesive wear initiates immediately at engagement and governs notch formation: polluted SiO₂ deposits act as an active bonding medium, and repeated bond formation/rupture removes extremely thin flakes of tool and coating material, evidenced by Al₂O₃ and Ti(C,N) fragments on the chip and by characteristic acoustic cluster waves. A new tool–chip contact model is presented, indicating that high pressure and high temperature within the polluted SiO₂ near the chip’s outmost side promote larger, stronger adhesive bonds together with the absence of ceramic particles near the rake in the notch area. Oxidation and diffusion are assumed to be secondary processes that become relevant after local coating loss, while adhesion remains the primary removal mechanism during early and intermediate stages. Full article