Search Results (399)

Objectives: To assess the association of structural biomarkers derived from optical coherence tomography (OCT) and choriocapillaris (CC) flow information with point-wise retinal sensitivity (PWS) measured by microperimetry (MP) in intermediate age-related macular degeneration (iAMD). Methods: Patients with iAMD received imaging with spectral-domain (SD)-OCT (Spectralis, Heidelberg Engineering) and OCT-angiography (OCT-A) (PLEX Elite 9000, ZEISS). In addition, MP examinations in photopic setting (MP-3, NIDEK) and mesopic background illumination (MAIA2, ICare) were performed. The thickness of the ellipsoid-zone (EZ) and the outer nuclear layer (ONL), as well as the volume of drusen and HRF, were segmented using deep-learning (DL)-based approaches. CC flow deficit percentage (FD%) was extracted from OCT-A slabs using a novel binarization method. Semiautomatic co-registration of MP examinations, OCT-A slabs, and OCT volumes was performed. Three exploratory models were calculated using multivariable mixed-effects models: (1) structure–function (SF) using structural OCT biomarkers, (2) flow–function (FF) utilizing OCT-A derived flow information, and (3) structure–flow–function (SFF) incorporating both OCT and OCT-A data. Model performance was evaluated using AIC and BIC criterion. Results: 19 eyes of 19 patients were evaluated, totalling 3297 MP-stimuli, 1873 B-scans, and 19 OCT-A slabs. Mean (SD) age was 76 (7) years, and sensitivity was 26.0 (3.36) dB in the MP-3 and 22.42 (3.64) dB in the MAIA2. Mesopic MAIA2 examinations showed significantly lower PWS values (−3.56 to −3.63 dB; p < 0.001). Drusen and HRF volume decreased PWS (−0.6 [95% CI: −1.04; −0.16] dB/nL; p = 0.007 and −9.56 [95% CI: −12.86; −6.26] dB/nL; p < 0.001), while ONL was positively associated with PWS (0.06 [0.05; 0.07] at an eccentricity of 5.2°; p < 0.001) in the SF model. CC FD% was not significantly associated with PWS in the FF and the SFF model (p > 0.05 in both cases). In the SFF model drusen volume (−1.69 [95% CI: −2.09; −1.29] dB/nL; p < 0.001), EZ (0.04 [95% CI: 0.02; 0.06] dB/µm; p < 0.001), and ONL thickness (0.03 [95% CI: 0.02; 0.04] dB/µm; p < 0.001) were significant predictors for PWS. The SF model exhibited the lowest AIC and BIC indicating best model performance. Conclusions: Structural parameters derived from SD-OCT such as HRF, drusen volume, and outer retinal layer thickness may be more closely associated with PWS, with CC FD% as an OCT-A-derived metric contributing limited additional explanatory benefit in cross-sectional analyses. Full article

Introduction: Breast cancer-related lymphedema (BCRL) affects quality of life (QoL) and increases healthcare costs. Resistance training (RT) is proposed as a preventive strategy, although its safety and effectiveness remain uncertain. Objective: To evaluate the effectiveness and safety of RT in preventing BCRL in women at risk. Methods: MEDLINE, Embase, CENTRAL, PEDro, and LILACS databases were searched from their inception to January 2025, along with the gray literature, trial registries, and preprints. Risk of bias was assessed using RoB 2, and certainty of the evidence (CoE) was assessed using GRADE. Primary outcomes were the occurrence of lymphedema and overall QoL; secondary outcomes included pain, upper limb function, range of motion (ROM), grip strength, and adverse events. Results: Eight RCTs (n = 1131) were included. The effects of RT on lymphedema and arm volume are very uncertain (very low CoE). For QoL, pain, ROM, and grip strength, the findings were inconsistent and uncertain (low to very low CoE). Adverse events were mild and transient, with no serious complications. Conclusion: RT is probably safe in women at risk of developing BCRL. Its preventive effectiveness is highly uncertain. Well-designed RCTs with standardized diagnostic criteria, patient-centered outcomes, and long-term follow-up are needed to establish their role in BCRL prevention with greater certainty. Ethics and dissemination: This study did not require ethical approval. The results will be disseminated through publications in peer-reviewed journals and academic presentations. Registration: PROSPERO (CRD42023455720). Full article

The transition to low-emission road transport in Europe depends not only on the growth of plug-in electric vehicle (PEV) uptake, but also on the timely expansion of publicly accessible charging infrastructure. This article provides a descriptive and diagnostic assessment of the relationship between electrification dynamics and public charging infrastructure development in Europe. The analysis combines a long-run descriptive window (2015–2024, with 2025 treated separately as a scenario observation) and a core diagnostic window (2020–2024) for which a consistent proxy of potential infrastructure strain—plug-in vehicles per public recharging point (VPP)—is available. The results show a strong increase in PEV share in new registrations, from 1.0% in 2015 to 20.92% in 2024, while the number of public recharging points rose from 67,064 to 900,000 over the same period. In the core sample, VPP declined from 15.24 in 2020 to 13.92 in 2024, which is consistent with a catch-up phase in infrastructure deployment after 2021. At the same time, the short-window relationship between PEV share, infrastructure scale and average CO₂ emissions of newly registered cars remains weak and unstable, indicating the role of additional structural factors. The article contributes a transparent, replicable indicator-based framework for describing infrastructure strain in aggregate European data. In policy terms, the findings support a shift from simple point-count targets toward functionally and spatially differentiated infrastructure planning, including interoperability, power structure, and accessibility in underserved areas. Full article

The accurate and explainable prediction of carbon emissions is crucial for the efficient operation of hybrid and electrified transportation systems and their integration with energy grids. An Explainable Spatio-Temporal Graph Neural Network (EST-GNN) is proposed for highly precise CO₂ emission forecasting using Lévy Flight-guided Optuna optimization. By modelling vehicles and their operational characteristics as nodes in a dynamic graph, the proposed framework can jointly learn timing and spatial correlations while sustaining interpretability. The accuracy of the EST-GNN model is compared with models based on one-hot encoded features, SMOTE-enhanced datasets, and ensemble regressors. Using a real-world dataset of 7385 vehicle registrations with 12 predictive features experiments are conducted. When applied the EST-GNN model outperformed all baseline and traditional models achieving the highest reliability (R² = 0.98754) while solving competitive error metrics (RMSE = 6.55, MAE = 2.556). There is strong indication that reasonable machine learning (ML) models can be used accurately to confirm their suitability for resource-prevented and real-time applications, while predictable ML techniques have relatively low reliability. The optimal solution ensures scalability, robustness, and independence of the deployment environment. The distribution analysis of best performing models develops the ability of EST-GNN, which accounts for the largest proportion of best results across evaluation metrics. To achieve superior predictive accuracy, graph-based learning, explainability, and advanced hyperparameter optimization are combined. EST-GNN provides a powerful tool for analyzing fleet emission levels, making energy-aware decisions, and planning sustainable transportation, while ML models continue to be a useful complement for deployment states with high computation costs and quick responses. Full article

Objective: This study aimed to compare the efficacy of immersive 360-degree Virtual Reality (VR) videos depicting local (Malaysian) versus overseas (Western European) natural environments on the mental health of medical students. The primary outcome was overall mental well-being (WHO-5), and the co-secondary outcomes were changes in anxiety, stress, and depression symptoms (DASS-21). Methods: A two-arm parallel randomized trial was conducted with 84 fourth-year and fifth-year medical students. Participants were randomized into two groups (n = 42 each) using a custom, gender-balancing minimization algorithm: Group 1 viewed local environments, and Group 2 viewed overseas environments. Each participant underwent two 15-min VR sessions spaced two weeks apart. Outcomes were measured at baseline (T0), after the first intervention (T1), and at the primary time point after the second intervention (T2). Data were analyzed using a repeated-measures ANOVA with Greenhouse–Geisser and Bonferroni corrections. Results: The VR intervention demonstrated a statistically significant improvement in well-being (p < 0.001, ${\eta }_p^2$ = 0.380) and a significant reduction in anxiety (p < 0.001, ${\eta }_p^2$ = 0.255) and stress (p < 0.001, ${\eta }_p^2$ = 0.311) across all participants over time. No significant change was observed in depression scores (p = 0.122, ${\eta }_p^2$ = 0.028). Notably, there were no statistically significant differences between the local and overseas groups for well-being (p = 0.399, ${\eta }_p^2$ = 0.011), anxiety (p = 0.593, ${\eta }_p^2$ = 0.005), stress (p = 0.945, ${\eta }_p^2$ < 0.001), or depression (p = 0.546, ${\eta }_p^2$ = 0.006). Conclusions: A two-session immersive VR nature intervention is effective for improving well-being and reducing anxiety and stress in medical students. The geographical familiarity of the environment did not significantly impact therapeutic effectiveness, suggesting that the restorative effects of virtual nature may generalize across different environmental and cultural contexts. Trial Registration: NCT07447310; retrospectively registered on 25 February 2026. Full article

Background: Glioblastoma exhibits profound intratumoral heterogeneity, with anatomically distinct tumor zones characterized by divergent molecular programs that drive therapy resistance. Whether magnetic resonance imaging (MRI)-derived radiomic features can capture these regional transcriptomic differences remains unknown. We aimed to determine whether subcompartment-level radiomic features associate with transcriptomic pathway enrichment scores derived from biologically approximate tumor zones. Methods: We matched 28 patients (mean age 58.5 years; 13/28 MGMT methylated) across the IvyGAP RNA-seq atlas and the IVYGAP-RADIOMICS datasets. Single-sample GSEA (ssGSEA) pathway scores were computed for 24 gene sets. Radiomic features (3920 per subcompartment) were reduced to 597. Nested leave-one-patient-out cross-validation (LOPO-CV) with Elastic Net served as the primary predictive analysis; linear mixed-effects models (LMM) provided exploratory associational analysis. Analyses used a biologically motivated but spatially non-co-registered zone-to-subcompartment mapping; all reported associations are zone-approximate. Results: Twenty-one of 24 pathways showed no predictive signal (R²_cv ≤ 0). Inflammatory Response (R²_cv = 0.185, 95% CI [0.071, 0.355], p = 0.008) was the only pathway supported by both the nested CV (FDR = 0.096) and the exploratory LMM (FDR = 0.024, ΔR² = 0.214 beyond subcompartment effects) analyses; the LMM association was robust to clinical covariate adjustment (likelihood ratio test p = 0.004). Angiogenesis (R²_cv = 0.209, 95% CI [0.028, 0.353], p = 0.006) reached nested CV significance (FDR = 0.096) but was not corroborated by the LMM (FDR = 0.445); it is therefore reported as a tentative single-framework signal requiring independent validation. T2-derived texture features were selected in 100% of folds for both pathways. Conclusions: Inflammatory Response is the only pathway supported by both analytical frameworks; Angiogenesis is a tentative nested-CV-only signal pending independent validation. The absence of signal for 21 of 24 pathways should not be interpreted as evidence of biological inaccessibility: at N = 28 (vs. N ≈ 240 required by Riley criteria), severe underpowering, attenuation from the non-spatial zone-to-subcompartment mapping, and methodological constraints each independently suffice to suppress real associations. Five of the 24 gene sets (the IvyGAP zone modules) are non-independent from the outcome data and cannot be interpreted as discovery. All reported associations are zone-approximate and may partly reflect macro-compartment (between-subcompartment) effects; validation in larger cohorts with spatially precise co-registration is essential. Full article

Background/Objective: Cardiac innervation plays a critical role in regulating myocardial function and enabling the heart to adapt to physiological and pathological conditions. Although the general features of sympathetic and parasympathetic innervation of the myocardium are well described, the spatial organisation of nerve fibres within the cardiac muscle remains incompletely characterised. This study aimed to develop and validate the SKUF (Slice–Keep–Unwrap–Fuse) protocol, a multimodal framework for mapping myocardial innervation through the integration of histological data and magnetic resonance imaging (MRI). Methods: The study was performed on the heart of a 7-year-old patient who died from rupture of a cerebral vascular malformation without evidence of cardiovascular disease. Prior to histological processing, post-mortem MRI was performed to provide a precise anatomical reference. The heart was sectioned into sequential transverse rings of 4 mm thickness, yielding 71 paraffin blocks. Histological sections (3 μm) were immunostained with antibodies against UCHL-1 to visualise nerve fibres and scanned using an Aperio AT2 system (20× magnification). Automated image analysis was conducted using the SVSSlide Processor module, which included tissue segmentation, colour-based nerve fibre detection, and sliding-window density mapping. Heatmaps were assembled into ring-based myocardial reconstructions and co-registered with MRI slices using combined rigid and deformable registration, followed by three-dimensional reconstruction of innervation patterns. Results: A higher density of nerve fibres was observed in the right ventricular myocardium compared with the left ventricle, whereas larger nerve trunks were identified in the epicardium of the left ventricle. Quantitative analysis revealed a pronounced longitudinal gradient of innervation, with minimal density in the apical region and progressive increases towards the mid-ventricular segments, where maximal density and spatial organisation of neural structures were observed. The atrioventricular groove exhibited the greatest heterogeneity of innervation due to the presence of large nerve trunks and ganglionated plexuses. Integration of histological maps with MRI enabled three-dimensional visualisation of spatial clusters of nerve fibres. Conclusions: The SKUF protocol provides a robust framework for integrating histological and MRI data to generate three-dimensional maps of myocardial innervation. This approach may facilitate the development of high-resolution anatomical atlases of cardiac innervation and support future studies of neurocardiac mechanisms of arrhythmogenesis and targeted neuromodulation. Full article

Artificial intelligence (AI), cloud computing, robotics, automation, and remote sensing technologies are all contributing to digital transformation in forestry. Improving on low-accuracy Global Navigation Satellite Systems (GNSS) positioning affected by multipath error and interception under forest canopies is critical for integrating smart and digital technologies into equipment in forest operations. In an era where lidar-derived individual tree locations are now increasingly available in digital forest inventories, a possible alternative approach to positioning resources such as people or equipment accurately could be to match locally-measured tree positions and attributes in the forest with an existing global reference map based on prior remote sensing missions, effectively using the trees themselves as satellites to circumvent the need for GNSS-based positioning. We evaluated a lidar-based alternative to GNSS positioning using predicted tree positions from local terrestrial laser scanning (TLS) matched with a global stem map derived from prior airborne laser scanning (ALS), a methodology we refer to as TreePS. The horizontal error of the TreePS system was estimated using 154 permanent single-tree inventory plots on the University of Idaho Experimental Forest with two different workflows based on two common R packages (lidR v. 4.3.0, FORTLS v. 1.6.2) using either spatial coordinates or spatial plus stem DBH predicted using one or both segmentation routines and a custom matching algorithm. Mean TreePS error using lidR for below and above-canopy segmentation had mean error of 1.04 and 2.04 m with 93.5% and 91.6% of plots with viable match solutions on spatial and spatial plus DBH matching. The second workflow with both FORTLS (TLS point cloud) and lidR (ALS point cloud) had errors of 1.09 and 2.67 m but only 57.9% and 54.2% of plots with solutions using spatial and spatial plus DBH, respectively. There is room for improvement in the matching algorithm but the TreePS methodology and similar feature-matching solutions may be useful for below-canopy positioning of equipment, people or other resources under dense forests and other GNSS-degraded environments to help advance smart and digital forestry. Full article

Children are often underserved by adult-oriented oral medicines, leading to off-label use and dosage-form manipulation that may compromise dosing accuracy. This review summarises recent advances in paediatric orodispersible tablets (ODTs), focusing on manufacturing technologies, superdisintegrants, taste masking, and in vitro disintegration testing. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidance and a protocol registered with the International Platform of Registered Systematic Review and Meta-analysis Protocols (registration number INPLASY2025110022), we searched PubMed, EMBASE, MEDLINE, Scopus, and Google Scholar for experimental studies on paediatric-relevant ODT formulation and evaluation. Two reviewers screened studies and extracted data on manufacturing methods, excipients, disintegration/dissolution testing, and key outcomes. Risk of bias was assessed using a six-domain framework. Overall, 65 studies met the inclusion criteria for this review. Direct compression was the dominant method, with freeze-drying, sublimation, spray-drying, nanoparticle-in-tablet systems, and semi-solid extrusion/3D printing also reported. Crospovidone, croscarmellose sodium, and sodium starch glycolate were the most common superdisintegrants, while natural and co-processed disintegrants showed promise as cost-effective alternatives. Disintegration was usually assessed using pharmacopoeial methods, with some modified set-ups to better simulate oral conditions. Paediatric ODT development is advancing rapidly. Broader translation requires harmonised disintegration testing, age-stratified acceptability reporting, and GMP-ready workflows, alongside benchmarking of superdisintegrants and attention to dose flexibility, packaging, and affordability. Full article

Archaeological sites characterized by the coexistence of extensive above-ground terrain and hypogeum structures present major challenges for accurate and comprehensive geospatial documentation. Conventional survey approaches—such as static terrestrial laser scanning (TLS), total-station measurements, and aerial photogrammetry—often suffer from operational constraints, particularly in the presence of narrow underground spaces, low or absent illumination, harsh environmental conditions, and restrictions on UAV deployment. Additional complexity arises when both surface and subterranean elements must be consistently georeferenced to a common global reference system, especially where establishing a traditional topographic–geodetic control network is impractical. Within the framework of the EIMAWA Egyptian–Italian Mission conducted by the University of Milano since 2018, the Geomatics group of the University of Bologna designed and implemented a multi-scale multi-technique 3D documentation workflow, with a prominent role assumed by Simultaneous Localization and Mapping (SLAM) mobile laser scanning. The approach was supported by GNSS measurements providing centimetric accuracy. SLAM was employed to document both the surface necropolis and multiple hypogeal tombs, enabling rapid acquisition of dense three-dimensional data in environments where traditional techniques are limited. All datasets were integrated within a unified reference system, resulting in a coherent, multi-layered spatial dataset representing both landscape and underground spaces. The results demonstrate that SLAM can produce dense point clouds that document at few-centimetric level accuracy and continuously both above- and below-ground contexts. Quantitative analyses of the co-registration and mutual alignment of multiple SLAM datasets confirm a high degree of internal consistency, further enhanced through post-processing refinement. Overall, the experience indicates that this solution represents a practical and reliable technique for complex archaeological surveying. Full article

In unmanned ground vehicle (UGV) swarm systems, comprehensive environmental awareness is critical for coordinated operations. Yet they are frequently deployed in occlusion-rich, constrained environments where multi-agent visual fusion is essential. However, existing methods are critically limited by offline-calibrated extrinsic parameters, hindering flexible deployment, and by a strong co-visibility assumption, which fails under severe occlusion. To overcome these constraints, we introduce an end-to-end, calibration-free framework for the joint registration of cameras and subjects. Our approach begins with a single-view module that estimates subjects’ poses and appearance features. Subsequently, a novel graph-based pose propagation module (GPPM) treats UGVs’ cameras as nodes in a graph, connecting them with edges when they share co-visible subjects identified via appearance matching. Breadth-first search (BFS) then finds the shortest registration path from any camera to a designated root camera, enabling pose propagation via local co-visibility links and global alignment of all subjects into a unified bird’s-eye-view (BEV) space. This strategy relaxes the stringent requirement of full co-visibility with the root node. A multi-task loss function is proposed to jointly optimize pose estimation and feature matching. Trained and evaluated on a synthetic dataset with occlusions (CSRD-O) collected by a UGV swarm system, our framework achieves mean camera pose errors of 1.57 m/8.70° and mean subject pose errors of 1.40 m/9.14°. Furthermore, we demonstrate a scene monitoring task using a UGV swarm system. Experiments show that the proposed method generates robust BEV estimates even under severe occlusion and low inter-view overlap. This work presents a purely visual, self-calibrating multi-view fusion perception scheme, demonstrating its potential to support cooperative perception, task-oriented monitoring, and collective situational awareness in UGV swarm systems. Full article

Background: The present study examines sensory differences in children referred for autism evaluations and explores associations between sensory differences, autism symptomatology, and adaptive skills. Using a clinically referred sample, this study captures the heterogeneity of diverse developmental profiles observed in everyday clinical practice and provides a nuanced understanding of sensory differences in an ecologically valid way in the context of autism assessments. Methods: Participants included 238 children (41 females/3–14 years), referred to a university-based autism clinic due to concerns related to autism. Autism diagnoses were confirmed using the Autism Diagnostic Observation Schedule-2, DSM-5 criteria, and expert clinical judgement informed by comprehensive multidisciplinary evaluation. Additional measures were collected to obtain information on sensory processing (Sensory Profile-2/SP-2) and adaptive functioning (Vineland-II/-3). Diagnostic outcomes were classified as autism (n = 121) versus non-autism (n = 117). Results: Non-autistic children scored higher than autistic children in sensory avoiding and sensitivity, with no group differences in sensory seeking or registration as measured by the SP-2. Correlational analysis showed negative associations between sensory differences and both autism symptomatology and adaptive functioning. Regression analysis further indicated that higher sensory differences predicted lower adaptive functioning, with sensory sensitivity showing the most widespread associations across communication, daily living skills, and socialization. Conclusions: Non-autistic children exhibited greater sensory avoiding and sensitivity than autistic children, which may possibly reflect co-occurring concerns such as anxiety or attentional difficulties (e.g., avoiding noisy environments due to anxiety rather than sensory sensitivity). Across groups, higher sensory differences showed consistent associations with lower adaptive functioning, highlighting the importance of assessing sensory behaviors in children with diverse clinical profiles. Full article

We investigate the potential of frequent repeat imagery acquired by the PlanetScope Dove small satellite constellation to overcome temporal and spatial limitations in automated surface topography mapping. While individual PlanetScope Dove stereo pairs produce low-quality Digital Surface Models (DSMs) with large height uncertainties, the high temporal frequency enables multiple DSMs to enhance accuracy through multiple-pair image matching. We present a fully automated SETSM framework by improving the quality of PlanetScope Dove DSMs based on SETSM Multi-Pair Matching Procedure (SETSM MMP). This framework enhances stereo pair quality through an optimized stereo pair selection by sequential conditional filtering and a Weighted Stereo Pair Index (WSPI). A novel inter-plane vertical coregistration, which minimizes scaling errors between single stereo pair DSMs, was developed to improve consistency and accuracy in DSM quality without reference surfaces. Applied to the cloud-obscured Pantasma crater region in Nicaragua, the optimized stereo pair selection automatically selects well-defined stereo pairs. The inter-plane vertical coregistration without existing reference surfaces achieves up to a 43% Root Mean Square Error (RMSE) reduction and 26% improvement in distribution within a 5 m vertical error. DSM quality correlated strongly with tile size, stereo pair convergence angle, asymmetric angle and terrain-dependent scale variability. The proposed framework provides fully automatic, high quality PlanetScope Dove DSMs without Ground Control Points (GCPs). Full article

Replacing inefficient lighting with energy-efficient alternatives is a proven way to reduce urban energy use, yet evaluating such policies remains challenging. For example, in 2013, New York City (NYC) initiated a program to replace 250,000 high-pressure sodium (HPS) streetlights with light-emitting diodes (LEDs) by 2017, but no subsequent evaluation was published. Here, we employ ground-based hyperspectral imaging (HSI; 0.4–1.0 microns, ∼850 bands) observations from the “Urban Observatory” (UO), obtained in 2013 and 2018, to quantitatively characterize this technological transition. Following co-registration, artifact removal, and source identification, we classified individual light source technologies using both a maximum correlation approach with spectral templates of known lighting types and a one-dimensional Convolutional Neural Network (1D-CNN) trained on 1321 manually labeled spectra, achieving an average precision of ∼92% for the 2013 data and ∼94% for the 2018 data across technology classes. Scene-level mixture modeling indicates a reduction in the HPS-to-LED brightness ratio from 1.15 (2013) to 0.27 (2018), demonstrating the capability of longitudinal HSI for evaluating urban lighting policy outcomes. Full article

Hyperspectral imaging in the long-wave infrared (LWIR) range enables identification of chemical compositions and material properties, but reconstructing 3D models from gimballed pushbroom sensors remains challenging because their unique acquisition geometry is incompatible with conventional photogrammetric software designed for frame cameras. This study presents a workflow for creating explorable 3D models from multi-angle LWIR hyperspectral imagery by co-registering hyperspectral line-scan data with simultaneously acquired RGB frame camera imagery using deep learning-based image matching. The co-registered images are processed in commercial photogrammetric software (Agisoft Metashape), and a texture-to-image mapping algorithm preserves correspondences between 3D model coordinates and original hyperspectral pixels across multiple viewing angles. Quantitative evaluation against reference data demonstrates that co-registration reduces geometric error approaching the accuracy of models built from high-resolution RGB imagery. The resulting models enable the retrieval of 8–50 spectral signatures per surface point, captured from different viewing geometries. This approach facilitates interactive exploration of angular variations in thermal infrared spectra, supporting material identification for non-Lambertian surfaces where single-angle observations may be insufficient for reliable classification. Full article