Search Results (732)

Rapid advances in nanomaterials, wearable biosensors, multimodal acquisition, and artificial intelligence have enabled increasingly integrated health-monitoring systems, yet their progression toward health digital twins remains unclear. We conducted a protocol-driven scoping review of original studies combining nano-enabled multianalyte or multimodal sensing, AI-supported analysis, and health applications. PubMed/MEDLINE, Scopus, Web of Science Core Collection, and IEEE Xplore were searched using a publication cutoff of 10 July 2026; platform execution was completed on 13 July 2026. Two reviewers independently screened 528 unique records and assessed 20 full-text reports. A 79-item charting form was jointly verified for 12 included studies. Nine studies reported reference-method or matrix-relevant analytical validation, nine included human-sample or on-body evidence, and six acquired longitudinal or continuous data. Under the author-proposed, corpus-specific functional classification, six systems were L0, five L1, and one L2; none of the 12 met the L3 or L4 functional criteria. No included study combined dynamic individual-state assimilation with prospective prediction or simulation, and none reported external-site validation or formal predictive uncertainty quantification. Because eligibility required nano-enablement, multiple analytes or channels, AI integration, and selected clinical domains, these findings do not estimate the prevalence or maturity of health digital twins in the wider literature. Progress requires longitudinal multimodal data, validated state updating, external generalization, confidence-aware AI, and prospective evaluation of governed feedback. Full article

Drug discovery is still an expensive and time-consuming process where finding the right drug associations is important for therapeutic development. In this paper, a new system is proposed for drug design called PharmaGraphFormer (PGF). It consists of five stages: (i) Data acquisition and preprocessing (DAP), (ii) Feature extraction and feature fusion (FEF), (iii) Molecular representation (MR), (iv) Multi-task prediction, and (v) Explainable artificial intelligence (XAI). This study employs a hybrid graph neural network (GNN)-transformer architecture that combines structural and sequence-based representations. Through DAP, several processes are executed, including the imputation or removal of missing values, outlier rejection, and class balancing. Next, through FEF1, features are extracted to represent the input data efficiently. Initially, compound-protein features are generated to document the interactions and relationships between chemical compounds and their corresponding target proteins. Secondly, drug characterizations are computed to encapsulate the physical, chemical, and structural attributes of each drug. After that, MR is performed using a graph-based molecule representation. Then, a novel model integrating GNNs and graph transformers, termed GNN-T, is proposed. Initially, GNNs represent the most promising deep learning models adept at processing non-Euclidean data. The Graph Transformer layer enhances atom representations by consolidating the representations of adjacent atoms through an attention mechanism. Finally, XAI is applied to explain the internal mechanisms of AI systems, rendering them comprehensible and interpretable. Across five independent runs, the proposed model achieved an accuracy of $0.963\pm 0.002$, a precision of $0.971\pm 0.002$, a recall of $0.958\pm 0.003$, an F1-score of $0.964\pm 0.002$, and a ROC-AUC of $0.993\pm 0.001$. These results demonstrate an outstanding performance when compared with all other models and emphasize that the proposed model is reliable in solving the problems of prioritizing compounds in line with the latest developments in AI-powered virtual screening and drug–target interaction modeling. Full article

Shear-wave time-of-flight (TOF) measurement enables controlled assessment of frequency-dependent wave propagation, but its feasibility in biological tissues remains insufficiently established. This study evaluated whether a custom shear-wave TOF device could detect frequency- and direction-dependent responses in ex vivo tissues. Three porcine liver samples and three chicken breast samples were examined. Chicken breast was measured with propagation parallel and perpendicular to visible muscle fibers. One-cycle sinusoidal excitations were applied at 40–160 Hz, with 50 acquisitions ensemble-averaged per sample–frequency measurement. TOF was estimated using Tx threshold detection and cumulative-energy-based Rx onset detection, and TOF-derived apparent shear-wave propagation speed was calculated from the Tx–Rx distance and the measured TOF. Frequency-dependent data were fitted using the Kelvin–Voigt fractional derivative model to obtain model-dependent KVFD fit parameters. Signal quality was assessed, and a preliminary descriptive comparison with HISKY EQTouch UD3000 (Wuxi Hisky Medical Technologies Co., Ltd., Wuxi, China) SWE was performed. All 63 averaged sample–frequency measurements satisfied the predefined primary-detection criteria. Mean apparent shear-wave speed was 3.145 m/s in porcine liver, 6.133 m/s in chicken breast measured parallel to the fibers, and 5.914 m/s in chicken breast measured perpendicular to the fibers, giving a parallel-to-perpendicular speed ratio of 1.037. Mean post-averaging, post-processing SNR ranged from 24.47 to 31.52 dB. The UD3000 comparison showed the same tissue ranking. The device detected frequency- and direction-dependent responses in averaged ex vivo signals, supporting its feasibility as a controlled research platform. Claims of absolute stiffness accuracy and intrinsic muscle anisotropy require independent calibration and validation. Full article

Escherichia coli is a highly diverse bacterial species that includes strains with significant pathogenic potential for both animals and humans. Urban pigeons (Columba livia domestica) are widely distributed and frequently interact with human environments. In contrast, exhibition pigeons are maintained under controlled conditions, providing an opportunity to compare bacterial populations across different ecological settings. Comparative data on virulence-associated gene patterns between free-living urban and exhibition pigeon populations remain limited, particularly when both populations are investigated within the same geographical setting. The present study aimed to investigate the distribution and co-occurrence patterns of selected virulence-associated genes in Escherichia coli isolates recovered from urban and exhibition pigeons. A total of 453 faecal samples (153 urban and 300 exhibition pigeons) were collected, from which 133 Escherichia coli isolates were recovered (65 from urban and 68 from exhibition pigeons), corresponding to an overall isolation rate of 29.4%. A representative subset of isolates was independently confirmed by species-specific PCR and Sanger sequencing before virulence gene profiling. Subsequently, all isolates were screened for six virulence-associated genes (fimC, ompA, tsh, hlyA, astA, and iroN) by PCR. Virulence gene prevalence, gene profiles, co-occurrence patterns, and virulence scores were analysed, and statistical comparisons between groups were performed. The most prevalent gene was ompA (88.0%), followed by fimC (66.2%) and iroN (32.3%), whereas hlyA was detected only sporadically (3.8%). The most common virulence profile was fimC + ompA (24.8%), followed by ompA alone (20.3%). Co-occurrence analysis identified fimC and ompA as the most frequently co-detected gene pair (79/133 isolates; 59.4%); however, their association was weak and not statistically significant (φ = 0.078, p = 0.371). Frequent co-detection with the iron acquisition-associated gene iroN was also observed. No statistically significant differences were observed in the distribution of the investigated virulence-associated genes between urban and exhibition pigeon isolates, although urban isolates exhibited a broader diversity of gene combinations. In conclusion, pigeon-derived E. coli isolates harboured diverse combinations of selected virulence-associated genes, with adhesion-associated genes being the most frequently detected determinants. These findings provide baseline data on the distribution of selected virulence-associated genes in pigeon-derived Escherichia coli isolates and support future studies incorporating additional virulence markers, whole-genome sequencing, and comparative analyses to understand their epidemiological relevance better. Full article

Adaptive learning systems need to sequence learning activities according to individual progress while respecting the prerequisite structure of the learning domain. This study proposes the Triadic Educational Knowledge Space (TEKS) framework for target-oriented adaptive sequencing in structured domains. TEKS integrates a prerequisite graph, a task-specific target set, and a recursively updated predictive distribution over prerequisite-consistent latent knowledge states. At each decision step, candidate items are evaluated using their predictive one-step acquisition probability, adjusted by graph-based target relevance. A simulation-based proof-of-concept study evaluated TEKS on an eight-node symmetric branching graph with two isomorphic target conditions, a six-node asymmetric converging graph, and a six-node linear prerequisite chain. Each condition involved 200 virtual learners, 90 interaction steps, and 30 independent repetitions. TEKS was compared with Prerequisite-Weighted Mastery Gap (PW-MG), a deterministic static schedule, and random selection under matched simulation conditions. In the branching structures, TEKS yielded higher final target mastery, mean target mastery over time, and final scope mastery than all three comparison strategies. In the linear prerequisite chain, TEKS and PW-MG showed similar target-level outcomes, whereas TEKS retained higher final scope mastery. Within the examined conditions, the difference between TEKS and PW-MG was most evident when multiple prerequisite-related candidates competed for selection. These results provide controlled proof-of-concept evidence for target-adjusted one-step acquisition scoring. Further evaluation is needed on larger and more varied prerequisite structures, under broader forms of model misspecification, and with authentic learner interaction data. Full article

The transition toward urban-scale digital twin and smart city management requires survey-grade 3D basemaps, yet conventional documentation remains time-consuming and prone to inaccuracies. While Terrestrial Laser Scanning (TLS) offers rapid 3D acquisition, capturing complex, GNSS-denied multi-story interiors frequently causes cumulative registration errors and isolated indoor–outdoor data silos. To address this, this study proposes a comprehensive typology-agnostic framework for developing high-fidelity digital twin basemaps. Treating the building as a unified spatial network, the methodology systematically mitigates error propagation through strategic linkage planning, rigid shell-first registration, continuous vertical core anchoring (via stairwells), and adaptive multi-source data fusion. Implemented on an adaptively reused heritage building, the developed basemap achieved an absolute georeferencing accuracy of 30.0 mm (RMSE) against an independent total station control network, alongside a mean relative error of 3.11 mm. Comparative analysis against legacy 2D CAD floor plans revealed simplified geometric representations and categorical dimensional deviations of up to 29.8 cm. Demonstrating its practical utility, the point cloud-centric geometric hub avoids forced geometric idealization, successfully supporting direct immersive visualization, architectural slicing (floor plans, sections, elevations), and multi-LOD algorithmic planar segmentation. This spatially constrained acquisition strategy bypasses legacy limitations, delivering a mathematically verified 3D reality capture essential for smart facility management, heritage conservation, and downstream semantic intelligence. Full article

Radiomics features are strongly sensitive to image acquisition, and separating that sensitivity from biological signal usually requires repeated patient scans that cannot be shared. We present an open, fully synthetic framework (radiomics-phantom) that maps and, as a proof of concept, corrects radiomics feature instability without any patient data. Deterministic three-dimensional texture phantoms are generated as anisotropic Gaussian random fields with known ground truth and an optional embedded lesion. An independently implemented feature core aligned with the Image Biomarker Standardization Initiative (IBSI) covers all eleven IBSI-1 feature families and matched all 482 published digital-phantom benchmark values within the applicable tolerances. An image-domain acquisition simulator applies point-spread blur, slice-profile averaging, dose-scaled correlated noise, resampling, and quantisation. Per-feature reproducibility across a sweep of fifteen textures (varying correlation length, anisotropy, and intensity scale) by nine acquisition conditions, with five independent noise realisations per stochastic setting, is summarised by the absolute-agreement intraclass correlation ICC(2,1), with a realisation-aware percentile-bootstrap 95% confidence interval for every estimate; constant features are excluded from estimation. Values span nearly the full range (median 0.13, 95% CI 0.03–0.19), and a hierarchical variance decomposition attributes a median 77% of per-feature variance to the acquisition condition and under 1% to stochastic realisation; the values are interpreted as exploratory rankings within this acquisition envelope. As a proof of concept, intensity variance and grey-level co-occurrence contrast under additive Gaussian noise were normalised using calibrated, invertible response models, returning them to their noiseless values on held-out data (median error below 4% across five textures and repeated noise realisations, and about 11% when the noise level is estimated from the degraded image itself), while features the models cannot describe are refused rather than corrected. All code and a 716-test suite are released openly and archived on Zenodo. The result is a reproducible, patient-data-free testbed for radiomics feature stability. Full article

Commercial artificial intelligence (AI)-assisted electrocardiography systems require local assessment of the acquisition-to-review pathway before patient-facing use. We conducted a single-site, simulation-based functional assessment of one commercial 12-lead ECG configuration. A physiological signal simulator, ECG acquisition unit, AI-assisted review platform, multiparameter monitor, and central monitoring system were used to examine 13 predefined rhythm and conduction categories, alarm behavior, local export pathways, a representative five-lead signal-display subset, and formative expert feedback. Expected category-level functional agreement was observed in 12 of 13 predefined categories. In the retained record, the discordant asystole scenario was categorized as lead-off/electrode disconnection, identifying a signal-integrity boundary that requires verification of electrodes, leads, cables, waveforms, and context before clinician adjudication. Signal-to-noise ratio and root-mean-square error values from 90 derived windows in five leads were reported descriptively. These findings cannot be generalized to the seven unmeasured diagnostic leads or to complete 12-lead signal fidelity. Local PDF, HL7 v2.x, DICOM, and platform synchronization were demonstrated without independent conformance or semantic-integrity testing. Formative input from six purposively selected experts comprised 30 ordinal ratings (median 5; observed range 4–5) and qualitative comments. These data do not constitute usability or educational-effectiveness validation. The study provides bounded functional evidence for the tested configuration and identifies requirements for prospective technical, human-factors, multivendor, and patient-level validation before clinical deployment. Full article

Real-time multi-sensor human activity recognition (HAR) requires accurate models and a system architecture capable of distributing computation, exchanging compact outputs, and maintaining temporal consistency across asynchronous streams. This paper presents a distributed HAR framework in which five wearable sensors are associated with local embedded nodes that perform acquisition, windowing, preprocessing, and convolutional neural network–long short-term memory (CNN–LSTM) inference. Each node transmits a timestamped six-class softmax vector, and a central node applies approximate synchronization and learned probability-level fusion. The framework was evaluated with ten participants whose data were not used for model development. It achieved 95.868% accuracy and a 95.642% macro-F1-score. During continuous operation, the system sustained 47.949 predictions/s, with a mean post-window end-to-end latency of 33.963 ms and a mean synchronization span of 13.788 ms. Relative to complete-window transmission, the numerical payload decreased by 97.69%, and central-node energy per prediction decreased by 52.2% compared with centralized real-time processing. Under 30% independent probability-message loss, accuracy remained at 94.31%. Full article

Plant diseases lead to substantial yield losses and pose a persistent threat to global food security, creating an urgent demand for high-throughput, accurate, scalable, and non-destructive disease-monitoring approaches. Remote sensing has emerged as a powerful tool, yet progress in plant disease detection remains fragmented across various disciplines, tasks, sensing methods, and data modalities. This review introduces a hex-view perspective to synthesise remote-sensing-based plant disease detection within a cohesive conceptual framework. Instead of treating sensing technologies, algorithms, and datasets independently, the hex-view incorporates six interconnected dimensions that jointly capture how biological processes, the measurement scale, and data characteristics constrain disease detectability, including when detection is possible and how reliably it can be achieved. The hex-view framework comprises six interconnected dimensions and forms an integrated framework called BTSCAD: (1) Biology (B): plant–pathogen interactions constituting the biological foundation of disease development and expression. (2) Task (T): the diverse disease-detection tasks and their corresponding research objectives. (3) Sensor (S): the sensing modalities that define the data acquisition type and richness of captured information. (4) Condition (C): the environmental conditions, sensing platforms, and spatial scales that shape disease observations and bridge controlled experiments and real-world deployment across leaf, canopy, plot, and regional scales. (5) Algorithm (A): the classical and state-of-the-art data-analysis algorithms used to extract disease-related information from sensor data. (6) Dataset (D): the data sources that underpin model development, evaluation, and generalisability. The hex-view perspective provides a clear framework for interpreting previous research and identifying future research directions. This review lays a structured foundation for developing robust, interpretable, and transferable disease-detection systems, supporting advancements in precision agriculture, high-throughput phenotyping, and sustainable crop production. Full article

Background/Objectives: Forensic age estimation increasingly relies on non-ionizing magnetic resonance imaging (MRI) evaluation of the knee growth plates. The original five-stage classification proposed by Dedouit et al. was developed using spin-echo proton-density-weighted imaging, and its applicability to gradient-echo sequences with different contrast mechanisms has not been fully established. This study aimed to apply the original, unmodified Dedouit classification to a volumetric three-dimensional Multiple Echo Data Image Combination (3D MEDIC) sequence and to generate corresponding age thresholds in a Turkish sample. Methods: Knee MRI examinations of 309 individuals (153 males, 156 females; age range 9.18–25.97 years) were retrospectively evaluated at a 3-Tesla field strength. Two experienced observers independently staged the distal femoral and proximal tibial epiphyses; intra- and inter-observer agreement were assessed using Cohen’s kappa. Spearman’s correlation and the Mann–Whitney U test, with rank-biserial effect sizes and Bonferroni correction, were used to assess the relationship between age and stage and between-sex differences, respectively. To provide forensically applicable thresholds, we additionally modeled the age at which the probability of complete fusion (Stage V) reached 50%, with 95% bootstrap confidence intervals, and calculated the sensitivity, specificity, and area under the curve (AUC) of Stage V for identifying individuals ≥18 years. Results: Agreement was very good for both epiphyses (kappa = 0.808–0.833). Age correlated strongly with stage for both the femur (rho = 0.70–0.76) and tibia (rho = 0.68–0.74). The 50%-probability age for complete fusion ranged from 15.23 years (tibia, females) to 17.43 years (femur, males). Stage V showed high sensitivity (0.96–0.98) but markedly lower and sex-dependent specificity for the 18-year threshold (0.48–0.58 in females versus 0.77–0.85 in males), indicating that Stage V alone is a poor sole criterion for confirming adult status in females. The youngest age at which Stage V was observed was 14.67 years in females and 16.07–16.16 years in males, markedly younger than thresholds reported using spin-echo imaging in the original Dedouit cohort. However, as an extreme-value statistic based on a single individual, this minimum should not be used as a forensic threshold in isolation. Conclusions: The Dedouit classification remains reproducible when applied to a 3D gradient-echo sequence, but the resulting age thresholds may be influenced by acquisition technique, population-specific factors such as socioeconomic status, or both. Consequently, thresholds derived from different MRI sequences or populations should not be assumed to be interchangeable without local validation. Full article

Diffusion-weighted imaging (DWI) has evolved from a niche research sequence into an increasingly valuable adjunct to conventional magnetic resonance imaging (MRI) in musculoskeletal (MSK) radiology. By providing qualitative and quantitative information on tissue microstructure through assessment of water diffusion and apparent diffusion coefficient (ADC) mapping, DWI offers functional insights beyond conventional morphological imaging. We aim to present the current evidence for DWI in MSK imaging organised around established applications and emerging applications, with particular emphasis on composition-related interpretive pitfalls relevant to differentiating tumours and other pathologies, and to review the technique’s evolving role in routine practice. This narrative review synthesises the current literature on the clinical utility of DWI in MSK imaging. It is structured in four parts: foundations and the tissue composition signal framework, including the basis of qualitative and quantitative assessment; established applications; emerging applications; and assessment of tissue composition-related interpretive as well as technical pitfalls, including those arising due to myxoid matrix, chondroid matrix, blood degradation products, organising thrombus, crystalline or mineralised material, keratinaceous debris, purulent content, cellular haematopoietic marrow, by using original cases from the authors’ institution, which have been confirmed either histologically or surgically. Applications are stratified by strength of evidence. Established applications of DWI include soft tissue abscess detection, differentiation of malignant from benign soft tissue tumours, differentiation of malignant from benign vertebral compression fractures, and myeloma staging and response assessment, as well as treatment response in soft tissue and bone sarcomas. Whole-body MRI with DWI for staging and response assessment in multiple myeloma is guideline-endorsed and supported by prospective multicentre data. Soft tissue abscess detection, soft tissue and bone tumour characterisation, and characterisation of vertebral compression fractures are supported by consistent evidence from multiple independent cohorts, although no universally transferable ADC threshold exists. The emerging applications, which are promising adjuncts supported by small, single-centre or heterogeneous studies with thresholds that have not been externally validated, include ADC ghost sign in osteomyelitis (high specificity but sensitivity of only 20%), peripheral nerve sheath tumour characterisation and surveillance in NF1 patients, peripheral neuropathy and plexopathy, predisposing conditions such as Li Fraumeni syndrome in paediatric cancers, inflammatory myopathy, and postsurgical assessment of residual disease, as well as opportunistic detection of venous thrombosis. Radiomics and machine learning approaches remain experimental. Recent technical advances, including reduced field-of-view imaging, multi-shot acquisition and improved fat suppression, have mitigated but not eliminated historical limitations of susceptibility artefacts and limited spatial resolution. DWI has become an important functional imaging technique that complements conventional MRI across a broad range of musculoskeletal disorders. Understanding the relationship between tissue composition and the diffusion signal is central to both interpreting DWI correctly and avoiding its characteristic pitfalls. DWI is best regarded not as a stand-alone technique but as one component of a multiparametric assessment, in which its functional information is integrated with conventional morphological imaging. Ongoing technical improvement and expanding clinical evidence are expected to further support its integration into routine MSK imaging and its development as a quantitative biomarker for diagnosis, prognostication, and treatment monitoring. Full article

This study presents the development and validation of an intelligent error compensator based on Long Short-Term Memory (LSTM) recurrent neural networks for dynamic weighing systems in copper concentrate belt conveyors. Conventional weighing systems fail to capture nonlinear temporal dynamics, leading to measurement inaccuracies during container filling operations. The methodology comprised data acquisition from load cells, speed sensors, and inclinometers; systematic hyperparameter optimization; and evaluation using Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and coefficient of determination (R²). Hyperparameter optimization identified an optimal configuration with one LSTM layer (20 units, learning rate 0.001, window size 20 steps). Evaluation on an independent test set showed that the compensator reduced MAPE from 8.5% (uncompensated system) to 3.01%, representing a 64.6% improvement, and reduced RMSE from 12.3 to 4.2 tons (65.9% improvement), with an R² of 0.95. Feature importance analysis confirmed physical consistency, with load cell voltage as the dominant predictor (42%). These results demonstrate that LSTM-based compensation significantly enhances weighing accuracy. The study provides a replicable framework for industrial metrology modernization, contributing to sustainable mining operations through material loss reduction and logistics optimization. While the proposed model has been validated offline using historical data, its deployment in the live production environment remains pending. Full article

Abdominal aortic aneurysm (AAA) is a progressive and often asymptomatic vascular disease associated with high mortality after rupture, but reliable circulating biomarkers for noninvasive detection remain limited. We aimed to identify and validate plasma protein biomarkers for AAA using an integrated proteomics-based approach. Plasma samples from 22 patients with AAA and 22 healthy controls were analyzed through data-independent acquisition (DIA) mass spectrometry. Differentially expressed proteins were subjected to bioinformatic analyses, including Gene Ontology enrichment, Kyoto Encyclopedia of Genes and Genomes pathway analysis, protein–protein interaction, and weighted gene coexpression network analyses. Candidate biomarkers were selected on the basis of differential abundance, diagnostic performance, and biological relevance and subsequently validated by enzyme-linked immunosorbent assay in an independent cohort comprising 93 patients with AAA and 83 non-AAA controls. DIA proteomics identified 111 differentially abundant proteins, revealing enrichment of pathways related to mitochondrial respiration, oxidative stress, inflammation, extracellular matrix remodeling, and proteostasis. Among the candidates, plasma CHRDL1 levels were significantly reduced, whereas OGN and CCL18 levels were significantly elevated in patients with AAA; these findings were consistently confirmed in the validation cohort. A combined three-protein model demonstrated strong diagnostic performance, with an area under the receiver operating characteristic curve of 0.890. These findings identify CHRDL1, OGN, and CCL18 as promising plasma biomarkers for AAA detection and further highlight mitochondrial dysfunction, chronic inflammation, ECM remodeling, and dysregulated proteostasis as key molecular features of AAA. Full article

Electrocardiogram (ECG) signals are essential for arrhythmia detection; however, they are frequently degraded by noise during acquisition, and their evaluation is vulnerable to data-leakage and patient-overlap issues that can compromise model assessment. Therefore, in this study, we propose an image-encoding-based arrhythmia classifier combined with a morphology-aware denoising autoencoder. We evaluate signals under a corrected, patient-independent protocol in which every model-selection decision was made on a separate validation partition. On a leakage-free test partition, the autoencoder achieved an SNR improvement of 5.63 dB and a correlation coefficient of 0.801, improving R-peak amplitude preservation and redetection accuracy under moderate-to-severe noise while introducing measurable morphology degradation when the input was already lightly contaminated. The proposed model encodes the denoised, beat-centered signal into images through an interleaved-grouping outer product with sorting and flipping, and classifies them with a multi-scale three-dimensional convolutional network. Under this protocol, the proposed model obtained the highest macro-F1 among five image-encoding architectures, but did not outperform four models operating directly on the denoised signal (macro-F1 32.5% versus 37.3–40.0%), indicating that the proposed encoding does not improve overall five-class classification under rigorous inter-patient evaluation. A controlled test showed that the encoding is exactly invariant to global signal-polarity inversion, unlike the sequential models. This targeted invariance, rather than a general accuracy advantage, is the contribution reported here. Full article