Search Results (741)

Background: The clinical value of the cerebroplacental ratio (CPR) for predicting adverse perinatal outcomes in unselected obstetric populations remains uncertain. We aimed to evaluate the prognostic performance of serial third-trimester fetal biometry and Doppler-derived CPR and to compare their ability to identify adverse perinatal outcomes and neonatal growth abnormalities. Methods: In this prospective longitudinal cohort study, 100 consecutive pregnancies from an unselected obstetric population underwent standardized ultrasonographic examinations at both 28 and 37 weeks of gestation. Fetal biometric measurements, estimated fetal weight (EFW), amniotic fluid index, umbilical and middle cerebral artery Doppler indices, and CPR were recorded. Maternal, obstetric, delivery, and neonatal data were collected prospectively. Receiver operating characteristic (ROC) curve analyses and parsimonious multivariable regression models, accompanied by 1000-sample bootstrap internal validation, were performed to evaluate the independent predictive capacity of fetal biometry and CPR metrics. Results: Gestational age-specific CPR percentiles were associated with expected physiological Doppler changes but showed limited associations with obstetric and neonatal outcomes. In separate parsimonious regression models optimized for event-per-variable ratios, neither 28-week nor 37-week CPR independently predicted composite adverse perinatal outcomes, NICU admission, low Apgar scores, or abnormal umbilical cord blood pH. Rigorous bootstrap internal validation confirmed that optimism-corrected area under the curve (AUC) values for CPR models remained close to chance (range: 0.463–0.507). In contrast, 37-week EFW independently predicted neonatal birth weight, while both 37-week EFW and EFW percentile demonstrated good discriminatory performance for identifying small-for-gestational-age neonates (AUC = 0.812 and 0.833, respectively; both p < 0.001). Maternal body mass index and late-pregnancy fetal biometry also showed moderate discriminatory performance for predicting large-for-gestational-age neonates. Overall, late-pregnancy biometry outperformed CPR for identifying growth abnormalities, whereas neither modality alone accurately predicted composite adverse perinatal outcomes. Conclusions: In an unselected obstetric population, serial third-trimester CPR provided limited additional prognostic information beyond routine fetal biometry. Late-pregnancy fetal biometry, particularly 37-week estimated fetal weight, demonstrated superior performance for identifying neonatal growth abnormalities. These findings support the continued use of conventional fetal biometry as the primary component of routine third-trimester surveillance, with CPR serving as a complementary rather than standalone Doppler parameter. Full article

Objectives: To evaluate temporal trends in ocular biometrics, patient demographics and surgical strategies for intraocular lens (IOL) localization in pediatric cataracts. Methods: This retrospective study included 291 eyes of 148 pediatric patients operated at a tertiary referral center between 2019 and 2025, comprising 219 operated eyes and 72 unaffected fellow eyes serving as intra-individual controls. Eyes were stratified into three chronological cohorts (2019–2020, 2021–2022, 2023–2025). Axial length (AL), white-to-white (WTW) diameter and central corneal thickness were analyzed in relation to the anatomical IOL placement. A generalized linear mixed model with a random intercept per patient accounted for the within-patient correlation between fellow eyes. Results: Mean AL increased from 18.91 ± 2.83 mm to 20.32 ± 3.19 mm (p = 0.004), whereas the increase in WTW diameter was not significant (p = 0.450). The age at surgery increased significantly across the periods (median 1.08, 1.33 and 3.13 years; p < 0.001), a general temporal effect that was not modified by the site of implantation (p = 0.32). Underlying etiology strongly influenced the approach: all eyes with Marfan syndrome received an iris-fixated IOL, whereas 90% of uveitic eyes remained aphakic. Femtosecond laser-assisted cataract surgery (FLACS) was used in 13 eyes and confined to the most recent period. Conclusions: Pediatric cataract surgery shows a progressive increase in the age at surgery that is not specific to any IOL localization, while the surgical approach is frequently determined by the underlying etiology. The increasing use of FLACS represents an institutional trend; the small number of cases and absence of outcome data preclude conclusions regarding its safety or feasibility. Full article

Background/Objectives: Glaucoma represents a progressive optic neuropathy and is the leading cause of irreversible blindness worldwide. The disease is classified as primary angle-closure glaucoma (PACG) or as primary open-angle glaucoma (POAG) depending on the iridocorneal angle. Although gonioscopy remains the gold standard investigation, anterior segment optical coherence tomography (AS-OCT) may provide important data about these structures, offering a quantitative assessment of the angle. The purpose of this study is to evaluate AS-OCT parameters in patients with primary angle-closure disease (PACD). Methods: This retrospective exploratory study included 75 eyes from 60 patients with PACD, classified as primary-angle closure suspect (PACS), primary-angle closure (PAC) and PACG. Participants underwent comprehensive ophthalmic examination: best-corrected visual acuity (BCVA), gonioscopy, AS-OCT, optic nerve OCT, visual field examination, and specular microscopy. Associations between AS-OCT angle parameters, intraocular pressure (IOP), and ocular biometrics were assessed using correlation analyses with Benjamini–Hochberg correction for the primary analyses and generalized estimating equation (GEE) models to account for inter-eye correlation. Results: In the overall PACD cohort, AS-OCT angle parameters were inversely associated with IOP, with all primary correlations remaining significant after multiple-testing correction. GEE analysis confirmed significant associations for TISA500, AOD500, and AOD750. Exploratory analyses in the PACS, PAC and PACG subgroups were not significant after correction for multiple testing, while anterior segment biometric parameters showed expected intercorrelations. Conclusions: Quantitative angle parameters demonstrated robust inverse associations with IOP in the overall PACD cohort. These exploratory findings support the complementary role of AS-OCT in the PACD anatomical evaluation and warrant confirmation in larger prospective studies to determine their clinical and prognostic relevance. Full article

The authentication mechanism can ensure the security of data in the industrial Internet. However, existing authentication schemes mainly target traditional network entities and lack specific designs for AI service security, leaving models at risk of integrity breaches and malicious tampering. To address this, this paper proposes a novel three-party authentication scheme for the AI service environment. This scheme establishes secure two-way authentication among users, gateway nodes, and AI service providers, while ensuring user anonymity and forward security, and providing comprehensive resistance to diverse attacks such as insider attacks and DDoS. By organically combining biometric verification with lightweight cryptographic operations, our scheme maintains operational efficiency while enhancing security. A detailed analysis shows that its computational overhead is approximately 0.065 milliseconds and the communication overhead is 2304 bits, significantly outperforming existing schemes. Full article

Palm-vein recognition is an attractive biometric modality for secure access control because its subcutaneous vascular patterns are difficult to observe and reproduce externally. However, existing studies optimize the recognition algorithm and the hardware accelerator in isolation, and rarely satisfy the on-chip memory and energy constraints of edge devices. This paper presents a hardware-aware co-design of a lightweight FPGA accelerator for palm-vein recognition, in which the network is shaped by the cost structure of the target fabric and the inference engine is organized around the resulting layer shapes. On the algorithm side, a hardware-aware neural architecture search with deployment cost terms is combined with divisor-aligned structured pruning and INT8 quantization-aware training. Structured pruning reduces the model parameters to 0.32 M and the MACs to 87.2 M while preserving recognition accuracy. On the hardware side, a task-specific design space exploration selects a $14\times 12$ systolic array and an output-stationary dataflow that keeps all feature maps and weights on chip and reduces the modeled buffer-access count by 34.6% relative to the best alternative stationary dataflow. Implemented on a Xilinx Zynq-7100 at 100 MHz, the deployed INT8 checkpoint attains an accuracy of 99.50%, with a PL inference latency of 33.03 ms and an energy efficiency of 33.27 FPS/W. Full article

The security of a signature scheme given in the standard model (SM) will be more stable and reliable than that given in the random oracle model (ROM). Fuzzy identity-based signature (FIBS) enables a user to generate a signature for a set of descriptive attributes, defined as $\omega ={\left\{{\omega }_j\right\}}_{j=1}^n$. Any attributes set ${\omega }^{\prime }={\left\{{\omega }_j^{\prime }\right\}}_{j=1}^n$ can validate the signature provided that the distance between $\omega$ and ${\omega }^{\prime }$ is below a predefined threshold. Most of the existing FIBS schemes are based on the ROM. It is of great significance to design a FIBS scheme based on the SM. In this work, we adopt fingerprint minutiae as the biometric modality and present a feature extraction algorithm $\mathcal{E}$ that transforms raw minutiae into quantized, privacy-preserving attribute sets, and we present a False Rejection Rate (FRR)–False Acceptance Rate (FAR) trade-off framework to calibrate matching threshold t, with adjustable n for qualified error performance. Subsequently, we present a novel and efficient FIBS scheme, which is proven to be unforgeable in SM for any polynomially bounded adversary under selective identity attack model. Compared to the existing FIBS schemes based on the ROM, our new FIBS scheme has a strong security model. Compared to the existing FIBS scheme based on the SM, our new FIBS scheme reduces total computation consumption by approximately $33.55\%$ and achieves a significant reduction in communication consumption, saving approximately $68.07\%$ of the message and signature size, which is suitable for biometric authentication. Full article

Aquaponic systems couple fish and plant production in recirculating loops, yet quantitatively assessed dynamic models for engineering analysis, scale-up, and operation under realistic conditions remain limited. Here, a modular process-based MATLAB R2026a framework is developed for the recirculating aquaponic system operated at the ASTREDHOR facility (France). The model links hydraulic transport with fish metabolism, nitrification, solids removal, and plant nitrate uptake, using monitoring-derived boundary conditions for temperature, dissolved oxygen, pH, and electrical conductivity. The fish-growth component was calibrated and evaluated against archived, temporally reconstructed biomass trajectories derived from campaign-based biometrics in three production cycles with different fish compositions and environmental regimes. Tank-wise $R^2$ values were 0.865–0.952 in the calibration windows and 0.700–0.921 in the fixed-parameter prediction windows, with prediction-period NRMSE values of 0.64–3.91%. These descriptive metrics quantify agreement on the reconstructed evaluation grid rather than performance over independently retained biometric sampling occasions. Complete corresponding time series were unavailable for TAN, ${\mathrm{NO}}_2^{-}$, ${\mathrm{NO}}_3^{-}$, total suspended solids, and plant uptake; these simulated outputs were therefore used only for mechanistic consistency assessment and exploratory scenario analysis, rather than independent validation. Local sensitivity analysis showed limited effects of temperature sensitivity (${\alpha }_T$), optimal temperature ($T_{opt}$), and minimum dissolved oxygen ($D{O}_{min}$) under observed conditions, whereas the feeding ratio ($TR$) and metabolic scaling exponent (n) strongly influenced simulated fish growth and nitrogen loading. Parametric sweeps provided preliminary, model-derived indications of feeding and biofilter-sizing limits under intensified loading; these thresholds require confirmation against independent water-quality measurements. The resulting framework is positioned as an off-line digital shadow with a fish-growth component assessed against reconstructed biomass trajectories and exploratory water-quality simulations. Full article

Background/Objectives: To evaluate the association between demographic, ocular biometric, and systemic comorbidity variables with corneal endothelial morphometric parameters in a Caucasian adult population undergoing cataract surgery. Methods: Cross-sectional study in a large cohort of patients who were candidates for cataract surgery with no other concomitant ocular pathology. Endothelial cell density (ECD), hexagonality (HEX), the coefficient of variation (CV), and the presence of guttae were assessed using specular microscopy (Tomey EM-4000). Biometric parameters were obtained by partial coherence interferometry (IOLMaster 700), and systemic comorbidities were recorded. Multiple linear regression models adjusted for age and sex were applied, along with stratified analyses according to axial length (AL). Results: A total of 1032 eyes from 1032 patients were included. The mean age was 75.9 ± 9.3 years. The cohort comprised 366 men (35.5%) and 666 women (64.5%). The mean ECD was 2113 ± 544 cells/mm², HEX 42.7 ± 21.6%, and CV 43.2 ± 9.6%. The prevalence of guttae was 15.4% (159 eyes). ECD correlated negatively with age (r = −0.148; p < 0.001) and positively with central corneal thickness (r = 0.091; p = 0.004) and anterior chamber depth (r = 0.064; p = 0.030). In the multivariate model, age was independently associated with lower ECD (β = −8.40 cells/mm²/year; p < 0.001), whereas male sex was associated with higher ECD and lower CV. No significant associations were found with AL, keratometry, or systemic comorbidities. Stratified analysis by AL group showed consistent patterns with no relevant differences. Conclusions: Corneal endothelial morphometry in Caucasian adults is primarily associated with age, sex, central corneal thickness, and anterior chamber depth, with no significant association with axial length or systemic comorbidities. Full article

Body weight is one of the important phenotypic traits in sheep breeding for evaluating growth performance, planning flock management practices, and determining economic efficiency. In this study, biometric and machine learning approaches were jointly employed to predict body weight in Karayaka lambs using morphometric characteristics. The research material consisted of a total of 150 Karayaka lambs, including 75 males and 75 females, raised in a private enterprise located in the Erbaa district of Tokat province. The study evaluated body weight (BW), heart girth (HG), abdominal girth (AG), diagonal body length (DBL), body length (BL), withers height (WH), rump height (RH), hip width (HW), chest width (CW), and body condition score (BCS). Relationships among variables were examined using Pearson correlation analysis and principal component analysis (PCA). Multiple linear regression, Ridge Regression, LASSO regression, Random Forest, and Gradient Boosting algorithms were applied to predict body weight. Additionally, variable importance analysis and the SHAP (SHapley Additive Explanations) approach were utilized to enhance model interpretability. The results demonstrated significant differences between sexes in body weight and the majority of morphometric traits (p < 0.05). Correlation analysis revealed that abdominal girth and heart girth had the strongest associations with body weight. According to PCA results, the first principal component explained the majority of the total variance and represented overall body size. Among the evaluated machine learning models, the Gradient Boosting algorithm achieved the highest prediction performance, with a training R² of 0.962 and a test R² of 0.862, together with the lowest prediction errors (RMSE = 1.320 kg and MAE = 1.034 kg). The Random Forest model ranked second, achieving a test R² of 0.828. Variable importance and SHAP analyses indicated that heart girth and abdominal girth were the most influential features in predicting body weight. In conclusion, morphometric traits can be effectively utilized to predict body weight in Karayaka lambs, and the Gradient Boosting algorithm represents a robust approach offering high accuracy and interpretability. Full article

Human gait datasets play a central role in the development and evaluation of computer vision models. However, the current dataset landscape remains highly heterogeneous, with inconsistent reporting of acquisition conditions, user variability, and sensing configurations, which limits reproducibility and hinders principled cross-dataset comparability. In this work, we propose a covariate-centered, modality-agnostic taxonomy for gait datasets, explicitly structuring variability across scene-level, user-level, and sensor-level factors. The proposed framework enables consistent characterization of datasets through a standardized set of covariates (A–R), bridging differences across application domains and sensing modalities. Following a systematic review protocol aligned with PRISMA 2020, we analyze 47 publicly available image- and depth-based human gait datasets spanning healthcare, biometric, and attribute-recognition application domains. Using the proposed taxonomy, we derive a quantitative analysis of covariate coverage, revealing systematic biases in current dataset design. Full article

Static, one-time authentication mechanisms such as passwords and PINs are increasingly inadequate for protecting mobile devices throughout an active session. Behavioral biometric continuous authentication (BBCA) addresses this gap by passively monitoring user-specific interaction patterns—keystroke dynamics, touch and swipe gestures, gait, and motion—to verify identity on an ongoing basis. This systematic review synthesizes 80 studies selected via a PRISMA-compliant protocol from IEEE Xplore, ACM Digital Library, Scopus, ScienceDirect, Web of Science, and SpringerLink (2017–2025). We examine behavioral and multimodal biometric modalities, machine learning approaches ranging from classical classifiers to deep sequence and transformer architectures, and their integration with intelligent personal agents, wearable devices, and IoT/edge infrastructures. Security analyses cover spoofing, adversarial and generative attacks, mimicry, and model-level threats including membership inference and reconstruction. Privacy-preserving mechanisms—cancelable biometrics, Bloom filter encodings, zero-knowledge proof protocols, federated learning, and blockchain-based identity management—are evaluated against practical trade-offs in energy consumption and latency on resource-constrained devices. Key research gaps are identified: the absence of standardized adversarial benchmarks, lack of end-to-end pipeline evaluations under simultaneous adversarial and privacy threat models, and limited user-centered studies on consent and acceptance of privacy-preserving mechanisms under frameworks such as GDPR. Recommended future directions combine adaptive multimodal fusion, privacy-preserving cryptography, energy-aware modality selection, and interdisciplinary human-centered evaluation to advance practical, resilient continuous authentication for mobile and assistant-enriched environments. Full article

Virtual reality (VR) headsets with built-in eye trackers can record binocular gaze, but prior biometric studies have largely emphasized monocular gaze or general spatial trajectories, leaving the identity value of interocular coordination largely untested. We test whether that geometry provides identity information beyond monocular gaze dynamics. Using the GazeBaseVR dataset, we derive disparity, vergence-dynamic, and fixation disparity features and evaluate them in cross-session identification, task transfer, sequence modeling, replay detection, and return-visit analyses. Adding binocular features to monocular features improves cross-session Rank-1 identification, while binocular features alone provide complementary but limited discrimination. Performance is strongest when enrollment and probe tasks match and weakens sharply across tasks. Per-recording disparity centering leaves much of the binocular-only result intact, and a matched-input temporal convolutional network (TCN) performs better with binocular input than with duplicated monocular input. The TCN embedding overlaps with the disparity and vergence features, and simple feature-level fusion does not improve Rank-1 performance. Left-right gaze consistency can reject simple monocular replay attempts in this constrained setting, but score query adaptation reduces detection performance. In this setting, binocular dynamics are best treated as auxiliary evidence for VR gaze biometrics rather than as a standalone authenticator. Full article

In healthcare facilities, patient wayfinding challenges—especially in complex, multi-unit or campus-scale healthcare environments—extend the patient journey and increase front-desk workload. This work investigates the integration of a Digital Twin-orchestrated, IoT-enabled architecture that links digital scheduling to in-clinic guidance through standardized tokens. The proposed approach relies on (i) an administrative mapping layer that binds unique QR identifiers to cabinets, specialties, clinicians, and human-readable location labels, (ii) an appointment confirmation workflow that issues a confirmation code and delivers an e-mail package including a QR token and an RFC 5545-compliant (Internet Calendaring and Scheduling Core Object Specification) attachment, and (iii) a kiosk-like model (embedded, single-board computer with camera-based QR scanning) that resolves tokens via REST endpoints and presents deterministic guidance using a finite-state-machine workflow with explicit fallback from appointment resolution to cabinet-level QR mapping. An optional biometric module is included for recurrent visits via a database linkage layer, enforced by a single-owner rule for the serial sensor interface to prevent concurrency faults. Scenario-based validation confirms end-to-end operability and robustness, with time–motion quantification scheduled for future on-site evaluation. Full article

With the rapid development of Vehicular Ad Hoc Networks (VANETs), Vehicle-to-Infrastructure (V2I) communication plays a critical role in Intelligent Transportation Systems (ITS). However, existing authentication and key exchange protocols face challenges such as high computational cost, large communication overhead, and security and privacy risks in high-speed mobile environments. To address these problems, this paper proposes a lightweight V2I authentication key exchange and ticket-based fast handover authentication protocol based on consortium blockchain and a Physical Unclonable Function (PUF). The proposed framework integrates PUF-based device binding, biometric-assisted user binding, PRF-based dynamic pseudonym update, target-RSU-bound handover tickets, and consortium blockchain-assisted auditability. To avoid privacy leakage on immutable ledgers, the blockchain stores only keyed pseudonym indexes, cryptographic commitments, timestamps, revocation states, and audit records, whereas biometric helper information, PUF-derived values, long-term secrets, handover keys, and session keys are protected in TPM/HSM or encrypted off-chain storage. Formal verification using ProVerif indicates that the revised protocol satisfies the modeled secrecy properties, injective mutual authentication for initial authentication and handover, and non-injective ticket origin authenticity for accepted handover tickets. In addition, the Real-or-Random (RoR) model is used to prove fresh session key indistinguishability under explicit pre- and post-Test freshness, PUF unpredictability, fuzzy extractor security, and hardware-protected secret assumptions. Analytical performance evaluation further shows the core cryptographic cost of the proposed scheme while explicitly separating and parameterizing deployment-dependent TPM/HSM, AEAD, blockchain lookup, and PBFT confirmation costs. Full article

Traditional authentication systems rely mainly on static login checkpoints such as passwords or one-time verification. However, the expansion of cloud services, mobile devices, and distributed digital platforms has exposed significant limitations in these approaches. Modern cyberattacks increasingly exploit credential theft, phishing, and session hijacking in order to bypass login-based security mechanisms. This study examines the use of behavioral biometrics and data-driven analytics in continuous authentication systems that verify user identity throughout an active session. Behavioral interaction signals such as keystroke dynamics, cursor movement patterns, touchscreen gestures, and device usage characteristics can form distinctive behavioral profiles for individual users. Machine-learning models can analyze these signals to detect deviations from established behavioral patterns that may indicate unauthorized access. The paper develops a conceptual framework for continuous behavioral authentication that integrates behavioral monitoring, anomaly detection, and scalable data-processing infrastructures. The analysis highlights both the cybersecurity benefits of behavioral authentication and the challenges related to large-scale behavioral data collection, including privacy protection and responsible data governance. Full article