Search Results (2,592)

The present study aimed to evaluate whether commonly used condition indices reflect parasite load and bacterial colonization in juvenile Cyprinus carpio under natural environmental conditions in the Lower Danube River. A total of 260 specimens were examined for parasitological, microbiological, and biometric parameters, including 20 individuals analyzed for bacterial communities. Twenty-three parasite taxa belonging to eight major taxonomic groups were identified. Ectoparasites were found on the gills, skin, and fins, with monogeneans and ciliates, notably Dactylogyrus ssp. and Trichodina ssp., representing the dominant groups. Infection intensity was generally low to moderate, and histopathological examination revealed only mild epithelial alterations, including focal hemorrhage and mucus hypersecretion in more heavily infected individuals. Microbiological analysis identified six bacterial taxa associated with the skin, with Aeromonas hydrophila being the most frequently detected species. Correlation analyses showed no significant relationships between parasite abundance and condition indices (Fulton’s K, Le Cren’s Kn, scaled mass index, and BMI), although a slight reduction in Fulton’s K was observed in infected individuals. These findings indicate a stable host–parasite–microbiota equilibrium under natural environmental conditions. The results provide baseline ecological data that contribute to understanding fish health dynamics in the Lower Danube River and may support future monitoring and management strategies. Full article

Background: The development of robust artificial intelligence (AI) models for diagnosing Temporomandibular Disorders (TMDs) is severely constrained by data scarcity and patient privacy regulations. Cone-beam computed tomography (CBCT), the gold standard for assessing osseous changes in the temporomandibular joint (TMJ), inherently contains sensitive biometric facial features, making de-identification difficult without losing critical anatomical information. This study aims to develop and evaluate TMJCTGenerator, a specialized latent diffusion model (LDM) framework designed to synthesize high-fidelity, diverse, and anonymous TMJ CBCT images. We hypothesize that this LDM approach can achieve superior anatomical fidelity and diversity compared to traditional generative adversarial network (GAN)- and variational autoencoder (VAE)-based methods, specifically in capturing fine osseous details within sagittal and coronal views of the mandibular condyle. Methods: A training dataset comprising 348 anonymized CBCT volumes was obtained in this retrospective comparative study to extract high-resolution sagittal and coronal regions of interest of the mandibular condyle. An independent test set of 39 anonymized CBCT volumes was further included. We developed a class-conditional LDM that integrates a pre-trained VAE for perceptual compression with a conditional U-Net for iterative denoising in the latent space. Performance was evaluated via qualitative anatomical fidelity assessment, Fréchet Inception Distance (FID), and a blinded Visual Turing test conducted by experienced clinicians to determine the distinguishability of synthetic images from real data. Results: Qualitative analysis revealed that TMJCTGenerator produced images with superior sharpness and anatomical consistency compared to baseline models, successfully reconstructing fine bone structures essential for diagnosing degenerative joint disease. TMJCTGenerator achieved lower FID scores than both VAE and GAN baselines. In the visual Turing test, clinicians were unable to reliably distinguish the generated images from real scans, and non-inferiority analysis confirmed that the synthetic data were statistically non-inferior to real data. Furthermore, TMJCTGenerator demonstrated the capability to generate diverse pathological conditions, ranging from normal anatomy to severe osteoarthritic changes. Conclusions: The proposed LDM framework effectively addresses the data scarcity and privacy bottlenecks in TMJ AI research by generating realistic, fully anonymous medical imaging data. TMJCTGenerator outperforms traditional generative methods in both visual fidelity and diversity, offering a viable solution for training downstream diagnostic algorithms. The source code and pre-trained models of TMJCTGenerator have been made open-source. Full article

Cross-age face analysis has emerged as an important topic in biometric recognition due to substantial facial appearance variations caused by aging. Nevertheless, most existing approaches primarily focus on face verification (1:1 matching) and frequently rely on explicit age annotations, which limit their applicability in large-scale retrieval scenarios. In this study, large-scale cross-age face retrieval (1:N matching) is investigated, and a Hybrid Metric Learning Framework (HMLF) is proposed to learn age-invariant and retrieval-oriented facial representations without requiring age labels. The proposed framework integrates Additive Angular Margin Loss (ArcFace) with supervised contrastive learning to enhance feature discriminability. Furthermore, a mixed triplet mining strategy is introduced to improve the effectiveness of hard sample selection. A memory bank-based InfoNCE formulation is incorporated to provide a large number of negative samples, and an uncertainty-based adaptive weighting scheme is designed to automatically balance multiple loss components during optimization. To better simulate realistic retrieval scenarios, an extended cross-age retrieval evaluation protocol is established. Extensive experimental results demonstrate that the proposed framework achieves superior retrieval performance across different backbone architectures. The results further provide systematic insights into the influence of backbone design, loss formulation, and optimization strategies on cross-age retrieval accuracy. Full article

Proteomic research in the genus Vitis has progressed from early biochemical studies of soluble proteins to high-resolution, quantitative analyses encompassing all major organs and derived products. This review provides a comprehensive synthesis of advances in grapevine and wine proteomics. In leaves, studies have revealed extensive remodeling of photosynthetic, antioxidant, and defense pathways under biotic (e.g., Plasmopara viticola, Erysiphe necator, Xylella fastidiosa, Candidatus Phytoplasma vitis) and abiotic stresses (drought, salinity, heat, light). Bud proteomics elucidated hormonal regulation and mechanisms of dormancy release, while root studies identified nitrate-dependent metabolic shifts and adaptive protein networks. Cell culture models enabled controlled investigation of elicitor responses, stilbene biosynthesis, and temperature-induced proteome changes. In berries, proteomics clarified developmental transitions from fruit set to ripening, emphasizing proteins related to secondary metabolism, vacuolar transport, and stress tolerance. Comparative analyses across cultivars and environments identified biomarkers linked to aroma, color, and texture. The wine proteome revealed selective persistence of grape-derived proteins (e.g., thaumatin-like proteins, chitinases) and yeast peptides influencing stability and sensory properties, while Botrytis cinerea infection significantly alters this balance by degrading PR proteins and introducing fungal enzymes. Altogether, the Vitis proteome emerges as a dynamic, multifunctional system crucial for understanding plant adaptation, enological quality, and biomarker discovery. Full article

Intercropping systems involving sorghum, grasses, and legumes can enhance forage production and improve sustainability under no-tillage systems. In the context of agricultural systems, the effective selection of rotational species is essential, as they contribute to soil system dynamics and provide feed for livestock. In this study, the dry matter production of grain sorghum (GS: cultivar A 9902), forage sorghum (FS: cultivar Volumax), and dual-purpose sorghum (DPS: cultivar Rancheiro) intercropped with Urochloa brizantha and dwarf pigeon pea was evaluated at five sowing densities (0 to 24 seeds m⁻¹) over two growing seasons (2018 and 2019), conducted in a randomized complete block design under autumn growing conditions. Biometric and productive traits of sorghum were assessed, as well as the dry matter production of the companion species, in order to understand interspecific interactions within the system. Sorghum dry matter yield was not affected by pigeon pea density, indicating high stability of the main crop. Grain sorghum (GS) and forage sorghum (FS) showed higher production in the first season (20,428 and 18,210 kg ha⁻¹, respectively), whereas dual-purpose sorghum (DPS) performed best in the second season (25,388 kg ha⁻¹). GS exhibited the highest panicle production, exceeding the other cultivars by up to 55%. Increasing pigeon pea density enhanced its biomass production but reduced Urochloa production by up to 50%; however, Urochloa showed better performance when intercropped with GS and FS. Sorghum morphological traits were not affected, and overall, the intercropping system maintained sorghum productivity while increasing total biomass, demonstrating potential for silage production and pasture establishment. Full article

Objective: This study aimed to evaluate maternal serum afamin levels in women with intrahepatic cholestasis of pregnancy (ICP), examine their relationship with fasting bile acid concentrations, and assess their association with perinatal outcomes. Methods: This prospective case-–control study included 80 singleton pregnancies followed at a tertiary perinatology center between October 2025 and March 2026. Forty women with ICP, defined by pruritus and fasting bile acids > 10 μmol/L, were compared with 40 healthy pregnant controls. Women with ICP were further stratified according to fasting bile acid levels as <40 and ≥40 μmol/L. Maternal serum afamin concentrations were measured using a commercially available enzyme-linked immunosorbent assay (ELISA) kit. Maternal characteristics, liver biochemistry, fetal biometric and Doppler parameters as well as obstetric and neonatal outcomes were compared. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of afamin for ICP, and logistic regression analysis was used to assess its association with ICP. Results: Baseline maternal characteristics were comparable between groups. Maternal serum afamin levels were significantly higher in the ICP group than in controls (6.18 ± 4.24 vs. 3.98 ± 1.95 ng/mL, p = 0.004). Afamin correlated positively with fasting bile acids (r = 0.372, p = 0.018), but not with transaminases, gestational age at delivery, birth weight, or neonatal outcomes. In logistic regression, afamin was independently associated with ICP (adjusted odds ratio [aOR] 1.260; 95% confidence interval [CI] 1.059–1.500; p = 0.009). ROC analysis showed poor discrimination for ICP (area under the curve [AUC] 0.634, 95% CI 0.51–0.76, p = 0.039), whereas afamin did not discriminate between subgroups defined by fasting bile acid levels (<40 vs. ≥40 μmol/L). The optimal cut-off value of 4.93 ng/mL predicted ICP with 55% sensitivity, 67.5% specificity, a positive likelihood ratio of 1.69, and a negative likelihood ratio of 0.67. Conclusions: Maternal serum afamin levels are elevated in ICP and show a modest association with fasting bile acid burden. Its discriminatory performance is limited, and it does not reliably distinguish patients defined by a ≥40 μmol/L threshold. These findings suggest that afamin reflects the maternal response to cholestasis rather than disease severity and may serve as a complementary biomarker. Full article

This work introduces a non-blind watermarking framework for color images to address tamper detection, particularly under geometric transformations. The proposed scheme fuses two watermarks, a personal signature and a biometric fingerprint, into a unified composite watermark embedded into the chrominance component of the cover image using a multi-level transform domain approach, discrete wavelet transforms (DWTs), discrete cosine transforms (DCTs), and singular value decomposition (SVD). By leveraging the rotation-invariant properties of scale-invariant feature transform (SIFT) and oriented FAST and rotated BRIEF (ORB) descriptors, the framework ensures robust tamper detection without requiring alignment, thus mitigating the limitations of conventional detection techniques vulnerable to transformation-induced tamper obfuscation (TITO). Extensive experimentation demonstrates that the method maintains high perceptual fidelity, achieving PSNR values ranging from 50 to 55 dB for embedding strength factor μ (0.01–0.04) and SSIM indices near 1 across multiple benchmark images. Furthermore, the scheme exhibits notable resilience to a range of image processing attacks and geometric distortion. Comparative evaluation reveals its superiority over existing grayscale, color, SIFT-based and DWT-DCT-SVD-based watermarking techniques, affirming its applicability in scenarios demanding secure, imperceptible, and transformation-invariant image watermarking. Full article

Brazil, as one of the world’s leading fruit producers, faces increasing challenges arising from climate change, particularly in avocado cultivation, where excessive solar radiation and high temperatures impair plant metabolism, yield, and fruit quality. This study evaluated the use of a calcium and magnesium hydroxide-based sunscreen in mitigating heat stress in eight-year-old ‘Hass’ avocado trees. The experimental design was a randomized complete block design in a 4 × 8 factorial arrangement, with five replicates. Sunscreen applications were performed at full bloom and at the initial fruit development stage (18 mm). Leaf temperature, fruit drop rate, yield-related traits, fruit classification, and the percentage of fruit lesions were evaluated. Applications of the calcium and magnesium hydroxide-based sunscreen at concentrations of 3.0% and 4.5% (w/v) reduced leaf temperature and improved fruit biometric attributes compared to the control, although the maximum fruit diameter was achieved at the 2.6% concentration. The 4.5% sunscreen concentration reduced leaf temperature and fruit drop in ‘Hass’ avocado trees by 1.5 °C and 24.5%, respectively, compared with the control and decreased the percentage of small and damaged fruits. The application of sunscreen improved fruit weight and the percentage of fruits with higher market value, while the fruit diameter presented higher values at intermediate concentrations. Full article

Biometrics technology is undergoing a paradigm shift from static single-modal authentication to continuous multimodal sensing, combined with higher-performing algorithms powered by new deep learning techniques. This editorial reviews cutting-edge advancements and trends in the field of biometrics in four dimensions—novel sensors, modalities, algorithms, and equipment—as well as summarizes the contributions to this Special Issue, “New Trends in Biometric Sensing and Information Processing” by grouping them into the corresponding aspects of breakthroughs in this field. Full article

Marine mucilage events are intensifying in semi-enclosed seas under accelerating climate- and nutrient-driven pressures, yet their ecosystem-level consequences for aquaculture-linked coastal habitats remain insufficiently documented. This study provides an integrated spatiotemporal assessment of water quality, phytoplankton community structure, and biometric responses of Mytilus galloprovincialis during and after the 2025 mucilage outbreak in the Gulf of Erdek (Sea of Marmara, Türkiye). Mucilage accumulation was associated with sharp increases in turbidity, total suspended solids, and particulate organic matter, alongside declines in dissolved oxygen and pH. Phytoplankton assemblages exhibited marked seasonal restructuring: the mucilage period was characterized by the coexistence of mucilage-forming taxa, non-toxic bloomers, and multiple harmful algal bloom (HAB) groups, including DSP- and ASP-related species, whereas post-mucilage conditions were dominated by non-toxic diatoms with substantially reduced HAB representation. The dinoflagellate species representing the May period in terms of abundance were Noctiluca scintillans and Prorocentrum micans; the diatom species were Chaetoceros radiatus, Cylindrotheca closterium, Pseudo-nitzschia pseudodelicatissima, and Thalassiosira rotula; and the coccolithophore was Phaeocystis pouchetii. Mussel biometric analyses revealed biometric indices and condition values markedly below regional historical baselines during the mucilage event, alongside reduced meat yield, followed by pronounced compensatory growth during the post-mucilage period. Our findings demonstrate that mucilage acts as both a physical and biological stressor, driving short-term ecological shifts in phytoplankton diversity and imposing substantial but reversible physiological impacts on mussel stocks. These results underscore the need for continuous biodiversity monitoring frameworks that integrate mucilage dynamics, HAB occurrence, and aquaculture resilience in regions vulnerable to climate-enhanced organic aggregate formation. Full article

Iris biometric systems are recognized as secure alternatives to conventional authentication methods, yet challenges such as variable illumination, noise, and intricate iris textures persist. To address these issues, our study presents a novel hybrid iris recognition framework that integrates advanced deep learning with a pioneering application of Multivariate Ensemble Empirical Mode Decomposition (MEEMD) for feature extraction—a method not previously applied in this context. Our framework first employs MEEMD to extract statistical features that capture the iris’s nonlinear and nonstationary variations. We then combine global semantic information from two pretrained convolutional neural networks—VGG16 and ResNet-152—with local micro-texture details encoded by Local Binary Patterns (LBP) to form a comprehensive feature representation. An efficient pre-processing and segmentation stage precisely isolates the iris region, and the resulting features are refined through dimensionality reduction techniques to yield a robust, compact representation. These features are subsequently classified using multiple models, each rigorously tuned via hyperparameter optimization. Experimental validation on benchmark datasets—including IITD, CASIA, and UBIRIS.v2—shows that our model achieves recognition rates of up to 98% on IITD, 97% on CASIA, and 97.30% on UBIRIS.v2, surpassing existing approaches. This work not only enhances iris recognition performance but also establishes a novel method that bridges advanced deep learning with innovative feature extraction for high-security applications. Full article

Three-Dimensional (3D) face reconstruction from monocular Red-Green-Blue (RGB) imagery remains a fundamental yet ill-posed challenge in computer vision, with applications in biometrics, augmented reality/virtual reality (AR/VR), and intelligent visual sensing systems. While deep learning has significantly improved reconstruction fidelity and realism, existing surveys primarily focus on network architectures in isolation, often overlooking how sensing conditions, data acquisition protocols, and geometric calibration influence reconstruction reliability and evaluation outcomes. This paper presents a sensor-aware, end-to-end review of deep learning-based 3D face reconstruction and introduces a unified modular framework that connects sensing hardware, data acquisition, calibration, representation learning, and geometric refinement within a coherent pipeline. The reconstruction process is organized into four stages: sensor-driven acquisition and calibration, landmark estimation and feature extraction, 3D representation and parameter regression, and iterative refinement via differentiable rendering. Within this framework, we examine how sensor characteristics, calibration accuracy, representation models, and supervision strategies affect reconstruction accuracy, perceptual quality, robustness, and computational efficiency. We further synthesize the reported results across widely used benchmarks using both geometric and perceptual metrics, highlighting trade-offs between reconstruction fidelity and deployment constraints. By integrating sensing-aware analysis with architectural evaluation, this survey provides practical insights for developing scalable and reliable 3D face reconstruction systems under real-world conditions. Full article

Aviation safety depends critically on pilots’ mental and cognitive states, particularly in high-stakes and complex operational environments where human errors cause most safety events today. This paper reviews current advances in real-time monitoring of commercial pilots’ cognitive states through physiological and neurophysiological signals and identifies methods applicable to enhance aviation safety and efficiency. In an increasingly complex and congested system, it is essential to investigate the relationships between pilots’ mental workload, stress, startle effect, and physiological parameters to highlight cognitive overload or deficiencies in real time. This systematic literature review was conducted according to PRISMA 2020 guidelines, using Google Scholar, Scopus, and PubMed, and identified 26 eligible studies. A targeted backward citation search screened 17 additional records, and two studies were added to the initial set. Twenty-eight records were therefore included and the review highlights a range of biometric indicators of pilots’ mental states with varying degrees of validity and operational applicability. Collectively, these studies offer a clear overview of state-of-the-art approaches, while also evidencing constraints related to intrusiveness and real-world feasibility. Physiological monitoring holds strong promise for enhancing pilot performance and safety by detecting early signs of overload and stress. However, its integration into operational aviation remains limited. Future research should prioritise longitudinal, in situ evaluations, multimodal data fusion, and pilot-centred design to ensure practical applicability, non-intrusiveness, and regulatory compliance, ultimately bridging the gap between academic research and cockpit reality. Full article

Face recognition technology implemented on blockchain platforms enhances the security and integrity of face embeddings (the numerical representations extracted from facial images). However, it encounters unique privacy challenges due to the transparent and immutable nature of blockchains. Face embeddings hold sensitive biometric data that, once compromised, cannot be changed like conventional passwords. This study offers a new framework for using the Internet Computer Protocol (ICP), a decentralized blockchain platform, to implement CosPEEPChain (blockchain-secured privacy-preserving face recognition using eigenface perturbation and CosFace). CosPEEPChain integrates eigenface decomposition with local differential privacy (LDP) to ensure the privacy of face embeddings, CosFace for cosine margin learning’s discriminative ability on perturbed eigenface representations, and blockchain to ensure transparent and tamper-proof storage of face recognition models. We present CosPEEP (privacy-preserving face recognition using eigenface perturbation and CosFace), which shows substantial improvements and maintains consistent performance over baseline PEEP (privacy using eigenface perturbation), with a mean accuracy of 96.77 ± 0.85% and stability (std = 0.31–1.28%) across a range of privacy budgets ($ϵ\in [0.5,8.0]$) on the LFW dataset. Statistical significance testing confirms CosPEEP surpasses PEEP in 11/16 privacy budgets (p < 0.05) with a mean improvement of +1.92%. We also present ArcPEEP, which uses additive angular margin loss (ArcFace) to compare margin-based improvements. We verify the attributes of the models on the chain. In total, CosPEEPChain uses fewer cycles compared to the baseline ICP face recognition. Full article