Search Results (868)

This study quantified projection-dependent geometric distortion induced by vertical camera angulation in two-dimensional (2D) dental image acquisition and evaluated its implications for integration with three-dimensional (3D) CAD/CAM and artificial intelligence (AI)-driven workflows. To our knowledge, this study is among the first to use quantitative methods to characterize projection-induced distortion across the dental arch as a function of vertical camera angulation. Fourteen fully dentate casts were photographed at nine standardized vertical angulations using a controlled acquisition setup based on the standardized occlusal plane angle (SOPA). Tooth surface areas were measured through digital tracing and analyzed with a mixed-effects model (α = 0.05). Significant associations were identified between vertical camera angulation and measured tooth surface area for all teeth except canines (p < 0.05 for all except canines). Anterior teeth demonstrated increased apparent surface area at superior camera angulations, whereas posterior teeth were more prominently represented at inferior angulations. Central incisors, lateral incisors, and first premolars exhibited maximal visibility above the occlusal plane, while second premolars and molars were more optimally visualized below it. These findings indicate that vertical camera angulation induces non-uniform, region-specific geometric distortion across the dental arch. From a computational perspective, these distortions represent a systematic source of variability in 2D photographic datasets used in CAD/CAM workflows, virtual smile design, and AI-assisted image analysis. Because modern machine learning systems depend on geometrically consistent input data, uncorrected projection-induced distortion may reduce the reliability and generalizability of downstream algorithmic outputs. Accordingly, the present findings establish a quantitative basis for recognizing projection-induced variability in 2D dental photographs and support future development of geometry-aware calibration strategies for 2D-3D digital integration. AI-assisted correction represents a future translational direction contingent upon explicit alignment between acquisition geometry, image formation, and computational modeling. Full article

Electronic systems are increasingly used to support pediatric gait assessment by enabling objective measurement of biomechanical parameters beyond traditional laboratory settings. However, although technological development has expanded in adult populations, the extent to which embedded technologies and human–machine interaction (HMI) modalities have been integrated into pediatric monitoring systems remains unclear. This systematic review synthesizes evidence published between 2015 and 2025 on electronic systems applied to pediatric gait biomechanics. The review followed PRISMA guidelines, was registered in PROSPERO (CRD420251230372), and adopted a descriptive synthesis approach. A total of 2619 records were identified, and after eligibility assessment and methodological quality appraisal using CASP, 34 studies were included in the final synthesis. The studies were examined according to system type, interaction characteristics, and biomechanical outcomes. The findings indicate a predominance of wearable architectures and inertial sensing technologies in the literature on electronic systems for pediatric gait monitoring. However, HMI modalities were rarely described, and most systems functioned primarily as passive data acquisition tools. Biomechanical outcomes focused mainly on motion-derived parameters, whereas region-specific plantar-load distribution was infrequently assessed, and no studies reported the use of force-sensitive resistors for zonal pressure monitoring. These findings suggest that future advances may depend on integrative approaches that combine multimodal sensing, interaction mechanisms, and functional load characterization. Full article

Flight dynamics modeling is a fundamental cornerstone of aircraft design, simulation, and control. Traditional approaches rely on aerodynamic look-up tables for numerical integration, which suffer from high data-acquisition costs, poor extrapolation capability, and difficulty in assimilating flight test data. This paper proposes an architectural integration of physics-informed neural networks (PINNs), graph neural networks (GNNs), and known flight mechanics equations for flight dynamics modeling. Without requiring aerodynamic coefficient labels, the method predicts flight state derivatives using state-transition data. The approach encodes the structural knowledge of flight mechanics equations into graph topology and a physics computation layer (PhysicsLayer), so that the neural network only needs to learn the unknown aerodynamic coefficients while all remaining physical relationships are computed by the governing equations. Using an F-16 fighter six-degree-of-freedom model as the verification platform, an ablation study involving Direct-MLP, PINN, PIGNN, and GNN is conducted. Results show that the PIGNN architecture improves single-step derivative prediction accuracy by 86.6% over Direct-MLP, 60.9% over pure PINN, and 90.8% over GNN. In 499-step (approximately 5 s) rollout state prediction, the PIGNN Core RMSE is 1.1554, with approximately linear error growth within the first 100 steps indicating well-controlled short-range error accumulation. The graph-structural prior enables the network to learn aerodynamic coefficients that closely match the F-16 reference aerodynamic database without aerodynamic coefficient supervision. The results demonstrate that combining graph-based dependency modeling with hard physical constraints is effective for interpretable flight dynamics surrogate modeling. Full article

NanoArduSiPM represents a paradigm shift in the ArduSiPM (Architected Detection Unit for Silicon Photomultipliers) roadmap, evolving from a standalone instrument into a high-density modular building block (36 mm × 42 mm × 3 mm, 7 g). This revision does not merely pursue miniaturization; it re-engineers the signal-processing chain to maintain high performance within a scaled-down footprint, enabling the transition from single-unit detection to scalable, distributed multi-detector systems. NanoArduSiPM is based on a three-layer architecture comprising an external scintillator and Silicon Photomultiplier (SiPM) detection module, a dedicated high-speed discrete analog front-end, and a System-on-Chip (SoC) for embedded acquisition and processing. The physical implementation adopts high-integrity PCB routing and rigorous isolation techniques designed to suppress digital–analog coupling, a critical requirement in such a compact form factor. This deterministic layout strategy provides the architectural foundation for time-tagging capabilities, currently under quantitative characterization, by addressing the fundamental sources of signal interference at the hardware level. Beyond hardware integration, NanoArduSiPM introduces the capability for extended firmware functionality, including event tagging via external inputs and the implementation of coincidence and veto logic. This framework supports the acquisition of multiple correlated histograms and allows multiple units to be interconnected on a shared SPI bus. By shifting from standalone operation to a coordinated, hierarchical architecture, NanoArduSiPM enables distributed detection schemes where event selection and correlation are handled natively within the system, reducing the dependency on external data acquisition electronics. The compact modular architecture, together with the high-performance discrete analog front-end and embedded data handling, makes NanoArduSiPM suitable for applications where low mass and low power consumption are critical, targeting applications such as space-based payloads, laboratory instrumentation, remote sensing, and large-scale distributed multi-channel detection systems. While no radiation-tolerance qualification of the complete system has been performed in this work, the microcontroller family used in the design is also available in radiation-tolerant variants, which may support future implementations targeting more demanding radiation environments. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder for which early detection and reliable monitoring remain major clinical challenges. Electroencephalography (EEG) combined with machine learning has attracted growing interest as a scalable and non-invasive approach to AD detection, yet reported classification accuracies vary widely across studies and are rarely comparable or clinically translatable. One important reason is that the analytical pipeline—from data acquisition to model validation—involves numerous methodological choices whose inter-stage dependencies and reproducibility implications are rarely made explicit. In this narrative review, we adopt a methodological chain framework to make these dependencies explicit, organizing EEG-based AD research into five sequential stages: data acquisition, preprocessing, feature representation, modeling, and validation. Choices at each stage can shape downstream analyses, inflate reported performance, and reduce cross-study comparability in ways that are difficult to detect when stages are assessed independently. These effects are particularly consequential in EEG-based AD research, where cohorts are typically small and biomarkers are subtle. We make three primary contributions: (1) we describe inter-stage methodological dependencies that may contribute to reproducibility problems and performance inflation; (2) we synthesize major sources of methodological variability across representative EEG–AD studies and evaluate their differential impact on spectral, connectivity, and complexity features; and (3) we provide practical, stage-aligned recommendations culminating in a minimum reporting checklist. Full article

Unmanned aerial vehicles (UAVs) are broadly used for high-throughput plant phenotyping, yet their long-term use in public-sector research is increasingly challenged by regulatory restrictions and reliance on proprietary platforms. This study presented a regulation-compliant, modular multi-sensor unmanned aerial system (UAS) designed to deliver flexible, high-quality phenotyping data without dependence on restricted ecosystems. A dual-mount, open-architecture payload integrated RGB, multispectral, and thermal sensors, enabling simultaneous acquisition of structural, spectral, and thermal information within a unified workflow. Field validation in a lantana (Lantana camara) breeding trial demonstrated high-precision multi-sensor data fusion and reliable trait extraction. Spatial co-registration achieved centimeter-level accuracy, with alignment errors of 0.88 cm (multispectral) and 3.23 cm (thermal) relative to the RGB reference. UAV-derived canopy height closely matched ground measurements (R² up to 0.98; RMSE as low as 1.57 cm), while canopy coverage estimates showed consistency across sensing modalities (R² = 0.99; RMSE = 0.02 m²). Calibrated thermal orthomosaics provided robust canopy temperature estimation (RMSE = 3.13 °C), supporting a quantitative assessment of plant physiological status. Together, these results demonstrate that a regulation-compliant, open-architecture UAV platform can achieve high accuracy in multi-modal phenotyping while maintaining flexibility and cost efficiency. This work demonstrates a scalable and sustainable framework for UAV-based phenotyping, enabling researchers to adapt to evolving regulations while advancing data-driven crop improvement. Full article

The accurate and efficient acquisition of rock mechanical properties is critical for ensuring the safety and efficiency of underground engineering construction. Traditional laboratory tests are characterized by long cycles, high costs, and an inability to reflect in situ mechanical properties, while existing deep learning models based on while-drilling data suffer from poor noise robustness, insufficient deep feature extraction, and low accuracy in synchronous multi-parameter prediction. To address these limitations, this paper proposes a hybrid deep learning model (CNN-LSTM-MoE) combining a convolutional neural network (CNN), a long short-term memory network (LSTM), and a mixture of experts (MoE) system. The model enables intelligent prediction of elastic modulus, Poisson’s ratio, and yield stress from while-drilling parameters. The proposed model integrates CNN’s local feature extraction capability, LSTM’s temporal dependency modeling capability, and the multi-expert dynamic fusion mechanism of MoE. Furthermore, it incorporates physical constraints from rock fragmentation mechanics and an adaptive multi-objective loss weight optimization strategy to comprehensively enhance the multi-parameter synchronous prediction performance. Experimental results demonstrate that the proposed model achieves coefficients of determination (R²) of 0.8965 for elastic modulus, 0.9193 for Poisson’s ratio, and 0.9813 for yield stress on the laboratory validation dataset, with a mean squared error (mse) of 4.0720. Its prediction performance significantly outperforms benchmark models such as TCN and Transformer time-series architectures. Ablation studies further validate the critical role of the integrated LSTM and MoE modules in improving model accuracy, with the MoE module contributing an average R² improvement of approximately 24%. This study not only provides an effective method for high-precision acquisition of rock mechanical parameters while drilling, but also offers a feasible solution based on numerical simulation for data augmentation to address the common issue of scarce labeled data in deep learning applications within engineering fields. Full article

Four-dimensional medical images introduce the temporal dimension to three-dimensional spatial data, enabling the dynamic characterization of organ motion and providing important support for disease diagnosis and functional assessment. However, due to constraints such as low-dose acquisition and prolonged scanning, the challenges faced in obtaining 4D medical images with high temporal resolution include insufficient spatial sampling, severe motion artifacts, and image blurring. Therefore, generating high-quality and temporally continuous intermediate frames while ensuring patient safety remains a critical challenge in 4D medical image interpolation. To address this issue, we propose the Relation-Aware Bi-directional Motion Estimation Network (RA-BiMENet) for 4D medical image interpolation, which enables the accurate prediction of intermediate frames at arbitrary time points. Specifically, RA-BiMENet consists of two key components: a spatiotemporal transform MLP (TS-MLP) module and a hierarchical spatiotemporal fusion (HSTF) module. The TS-MLP module performs bi-directional motion estimation in a pyramid-recursive manner, where a relation-aware multi-scale MLP (RAM-MLP) unit is introduced to model local correlations and multi-scale dependencies for accurate nonlinear motion estimation. Based on the estimated transformations, the HSTF module hierarchically integrates cross-temporal features through forward warping and self-attention, thereby enhancing local detail restoration while preserving global temporal consistency. Experimental results demonstrate that RA-BiMENet outperforms state-of-the-art methods on multiple quantitative evaluation metrics and is capable of generating high-fidelity and temporally coherent interpolated frames under complex deformation scenarios, validating its effectiveness and superiority for continuous-time 4D medical image interpolation. 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

Nyaope is a highly addictive street drug that is widely used in South Africa, particularly in urban and peri-urban settings. Although it is traditionally consumed by smoking, increasing injection use has raised serious public health concerns due to an elevated risk of bloodborne viral infections and other drug-related health complications. The composition of nyaope is highly variable, frequently adulterated, and continually evolving, thus highlighting the need for detailed chemical characterization to support forensic investigations and public health interventions. An exploratory study design was conducted using eight nyaope samples seized from six sites within the City of Tshwane Metropolitan Municipality that were provided by the South African Police Service Forensic Science Chemistry Laboratory (SAPS-FSCL). Samples were analyzed using Ultra-High-Performance Liquid Chromatography coupled to Quadrupole-Time-of-Flight Mass Spectrometry (UHPLC-qTOF-MS) operated in data-dependent acquisition mode under positive ionization. Raw data from the methanolic extracts of nyaope was converted to mzML format and processed using SIRIUS software for compound annotation based on isotope pattern ranking and fragmentation analysis. Chemical profiling revealed multiple opiate-related compounds, including noscapine, heroin, papaverine, and codeine. Molecular networking revealed chemically diverse yet structurally related metabolites consistent with a poppy-derived botanical origin. In addition, multiple synthetic pharmaceutical adulterants were detected. Notably, one sample contained formaline, a toxic rodenticide structurally related to protopine, highlighting the risk of misidentification using less advanced analytical approaches. This study demonstrates the value of advanced computational metabolomics, including molecular networking and machine-learning-assisted mass spectrometry interpretation, for comprehensive characterization of complex illicit drug mixtures. These approaches enhance forensic accuracy and support informed public health and law-enforcement responses. Full article

Late gadolinium enhancement cardiovascular magnetic resonance (LGE-CMR) permits non-invasive quantification of myocardial fibrosis; however, automated scar segmentation remains challenging due to limited expert annotations and reduced image quality caused by acquisition noise and artefacts. We investigate two related questions: (i) whether inversion of a stochastic Gaussian diffusion process can reproduce the texture characteristics of low-signal-to-noise-ratio (SNR) LGE imaging, and (ii) whether the resulting synthetic data can improve automated fibrosis segmentation in annotation-limited settings. To this end, we introduce a mask-conditioned denoising diffusion probabilistic model (DDPM) that synthesises high-fidelity 2D short-axis LGE-CMR slices from three-class label maps (background, myocardium, scar), and we employ these synthetic images for training-set augmentation. The impact of augmentation was assessed using the nnU-Net v2 segmentation framework and benchmarked against exemplar-guided image synthesis with CoCosNet-v2 under identical data partitioning. On a held-out test set trained with 100 real cases, inclusion of 300 diffusion-generated cases increased the scar Dice coefficient from 0.173 to 0.271 (+56.7%), and the scar recall from 0.173 to 0.363, demonstrating enhanced sensitivity to fibrotic lesions. For comparable training budgets, diffusion-based augmentation consistently outperformed GAN-based augmentation, although performance improvements were non-monotonic with respect to the real-to-synthetic data ratio and attenuated as the size of the real dataset increased. A four-axis noise-fidelity analysis (spectral content, signal-dependent variance, short-range spatial correlation, distributional shape) further shows that the DDPM reproduces scanner-specific noise statistics substantially more faithfully than the GAN baseline, providing a mechanistic account for the augmentation gap. Full article

Medical image federated learning has emerged as a practical solution for multi-center collaboration without centralizing sensitive data. However, the dominant source of heterogeneity in medical imaging is often not merely at the statistical level but also at the mechanism level, arising from scanner vendors, acquisition protocols, reconstruction pipelines, and annotation styles. Such heterogeneity encourages models to rely on site-specific shortcuts rather than pathology-relevant signals, which leads to poor external-site generalization. To address this problem, we propose CarPe-FL, which is a causal representation-based personalized federated learning framework for medical imaging. CarPe-FL maps images into a latent factor space, estimates client-specific latent causal structures under server-side management, clusters institutions according to structural similarity, and constructs cluster-wise global causal backbones. These backbones are then injected into federated representation learning through structure-aligned masking and edge-wise personalization, while personalized heads capture institution-specific prediction behavior. In this way, CarPe-FL aims to suppress shortcut-dependent pathways while preserving clinically meaningful local adaptation. The proposed framework is expected to provide a principled solution for robust, personalized, and interpretable federated learning in multi-center medical imaging. Full article

Simulation is increasingly recognised as a strategic approach in nursing education for developing clinical competencies within safe learning environments. However, there is limited understanding of how virtual patient simulation supports the early development of clinical reasoning from the perspective of nursing students. Aim: To explore the perceptions of first-year undergraduate nursing students regarding the development of clinical reasoning and collaborative decision-making through virtual patient simulation. Methods: A qualitative, descriptive, and exploratory design was adopted. Semi-structured focus groups were conducted with 73 first-year undergraduate nursing students. Data were analysed using thematic content analysis following Bardin’s approach. Results: Students perceived virtual patient simulation as a meaningful and high-impact learning strategy. Realism, interactivity, and group collaboration emerged as key strengths. Engagement with dynamic clinical scenarios supported the integration of theoretical knowledge into practice, enhanced prioritisation skills, and promoted structured clinical reasoning. Collaborative learning facilitated shared reflection and collective problem-solving, while immediate feedback enabled learning through error within a psychologically safe environment. Participants also reported increased confidence and autonomy in decision-making. At the same time, students identified limitations related to software constraints and the alignment between automated assessment and their reasoning processes. Conclusions: Group-based virtual simulation appears to support the early structuring of clinical reasoning, extending beyond technical skill acquisition to foster reflective and collaborative practice. Its educational value, however, depends on intentional curricular integration and strong pedagogical alignment including structured facilitation, alignment between assessment and learning objectives, and opportunities for guided reflection. These findings contribute to a process-oriented understanding of how novice learners make sense of clinical reasoning in simulated contexts. Full article

Reliable identification of deceased individuals may be difficult when conventional biometric methods such as facial recognition, fingerprint analysis, or DNA profiling cannot be applied. In such cases, medical imaging records acquired during a person’s lifetime may serve as an alternative source of identifying information. Certain anatomical structures visible in computed tomography (CT), including the sphenoid sinus, exhibit considerable inter-individual variability while remaining relatively stable within the same individual. This study investigates the feasibility of using sphenoid sinus morphology as an anatomical biometric for automated identification from head CT scans. Identification is formulated as a ranking problem in which a query CT examination is compared with a reference database using geometric descriptors derived from segmentation masks, reducing dependence on CT intensity values. The dataset consisted of CT scans from 816 individuals acquired in two patient positioning modes: Head First Supine (HFS) and Head First Prone (HFP). Several deep learning architectures, including YOLOv8 variants, YOLO11L-seg, UNet++, DeepLabV3+, HRNet, and SegFormer-B2, were evaluated for sphenoid sinus segmentation. Based on F1-score performance and cross-mode stability, YOLO11L-seg was selected and further trained to construct a database of binary masks representing individual sphenoid sinus anatomy. Identification was performed using pairwise mask comparison based on the Intersection over Union (IoU) metric. To reduce the influence of segmentation artifacts and slice-level variability, the final similarity score for each candidate was computed as the average of the four highest IoU values across slice comparisons. Individuals were ranked according to similarity, and identification was considered successful if the correct subject appeared among the top five candidates and exceeded a predefined similarity threshold. The proposed approach achieved Top-5 identification accuracies of 97.27% for HFP and 87.67% for HFS acquisitions. These results demonstrate the feasibility of using sphenoid sinus geometry as a stable anatomical biometric for automated identification. The key contribution of this study is the introduction of a ranking-based identification framework that utilizes anatomical biometrics derived from CT data for reliable patient matching. Full article

Shot peening is a critical surface treatment for improving the fatigue resistance of aircraft propeller blades operating under complex cyclic loads. While accurate coverage evaluation is essential for quality assurance, its development is severely hindered by a fundamental bottleneck: the extreme scarcity of annotated datasets in this niche aerospace domain, where data collection is costly and low-frequency, as each acquisition requires the actual peening of high-value components. Consequently, existing practices are restricted to subjective manual inspection or conventional segmentation methods that lack robustness under complex textures. To bridge this gap, this study develops an integrated automated surface evaluation framework, termed DT-ZSAM (Dual-Threshold Zero-shot Assessment Model), which circumvents the data-dependency bottleneck by leveraging the zero-shot capabilities of the Segment Anything Model (SAM) within a custom-designed prompt-generation pipeline. To ensure end-to-end automation without manual intervention, the framework identifies candidate regions via a dual-threshold scheme in grayscale and brightness domains and extracts representative prompt points through density-based analysis refined by DBSCAN clustering. Experimental results demonstrate that the proposed framework achieves precise segmentation without requiring any pixel-level annotated training data. Notably, the proposed framework yielded a coverage rate of 30.57%, aligning closely with the expert visual consensus (25–35%), whereas the standard commercial instrument (TCV-2A) significantly overestimated the coverage at 62.33% due to its sensitivity to surface textures and fixed calibration logic. This framework provides a robust and pragmatic solution for high-stakes industrial quality control, offering a reliable path for automating inspection in domains where large-scale data acquisition is practically unfeasible. Full article