Search Results (2,600)

Human motivation is governed by a long-memory cognitive process in which the depth of temporal integration—how far into the past the system draws upon accumulated experience—is not fixed, but dynamically compressed under cognitive stress. Despite extensive empirical evidence that acute stress impairs working memory and narrows temporal integration in decision-making, no existing mathematical framework has formally coupled the memory depth of the governing operator to a physiologically grounded stress indicator. To address this gap, we propose a stress-adaptive variable-order fractional model for motivational intensity $M\left(t\right)$, in which the Caputo fractional order $\alpha \left(t\right)$ varies inversely with an aggregated stress indicator $\sigma \left(t\right)$ through the Hill-type coupling $\alpha \left(t\right)={\alpha }_{min}+({\alpha }_{max}-{\alpha }_{min})C/(C+\sigma \left(t\right))$, thereby encoding the empirically documented shift from deep integrative to shallow heuristic processing as cognitive load increases. Rather than deriving the model by algebraic manipulation of a differential equation, we formulate it directly as a causally consistent type-III Volterra integral equation, in which the memory kernel is evaluated at the history time s, ensuring that the weight assigned to each past state reflects the memory depth that was physiologically active when that state was experienced. Well-posedness is established rigorously via the Banach fixed-point theorem with explicit contraction constants, uniform boundedness and non-negativity of solutions are derived through the fractional Gronwall inequality, and numerical solutions are computed using an Adams–Bashforth–Moulton predictor–corrector scheme adapted to the variable-order kernel. Five numerical experiments demonstrate that stress-induced variation in $\alpha \left(t\right)$ produces qualitatively richer dynamics compared with the tested constant-order baselines: the proposed model achieves a steeper peak decline rate (0.48 versus 0.19–0.45), a larger burnout gap (3.15 versus 1.92–2.81), and faster recovery to ninety percent of peak motivation (4.2 versus 3.9–7.3 time units), while the empirically observed numerical convergence approaches $O\left(h^2\right)$ for sufficiently small step sizes. The framework offers a principled phenomenological substrate for memory-adaptive cognitive modelling, with direct implications for stress-aware intelligent tutoring systems that are capable of inferring $\alpha \left(t\right)$ in real time from biometric signals such as heart rate variability or galvanic skin response, and adjusting instructional complexity accordingly. Empirical calibration against learning-analytics and psychophysiological datasets, together with stochastic extensions for probabilistic burnout-risk prediction, are identified as immediate priorities for future research. Full article

Face recognition technologies are increasingly deployed in transport infrastructures to improve efficiency and security, but they raise significant privacy and data protection concerns. This study reviews how privacy-preserving face recognition techniques can address these challenges in real-world settings. Using a systematic literature review approach, the paper analyses research across technical, operational, and governance perspectives. The findings show that while advanced methods such as encryption, federated learning, and de-identification can reduce data exposure, they are rarely implemented in operational systems, which tend to prioritize performance and scalability. At the same time, governance-focused studies emphasize issues such as proportionality, accountability, and fundamental rights, often without clear links to technical solutions. Overall, the review highlights a fragmented landscape and a gap between research and practice, underscoring the need for integrated approaches that align privacy-preserving techniques with practical deployment constraints and regulatory requirements. Full article

Biometric sensing systems enable accurate identity recognition using unique physiological traits. These systems can be unimodal (single trait) or multimodal (multiple traits, such as iris and fingerprint). Biometric templates, digital representations of these traits, enhance security over traditional methods but are vulnerable to attacks. Unlike passwords, compromised templates cannot be replaced, necessitating robust protection. Various security schemes exist, including cancellable biometrics, biometric cryptosystems, sensing technology, and biometrics in the encrypted domain. Cancellable biometrics apply transformations, such as biometric salting, to obscure the original data. Biometric cryptosystems integrate cryptographic techniques, including key generation and key binding, to enhance security. Biometrics in the encrypted domain, such as homomorphic encryption, ensures data remains encrypted during storage and computation. This survey focuses on the fuzzy vault method, a key-binding biometric cryptosystem. It analyses its applications, security performance, and associated challenges across different domains. By analysing advancements in fuzzy vault mechanisms, this paper provides insights into enhancing sensor-based biometric security. The study aims to serve as a reference for researchers exploring secure and efficient biometric authentication methods, ensuring robust protection against unauthorised access while maintaining the integrity and usability of biometric data in real-world applications. Full article

Current biometric tools, with facial expression analysis capabilities, enable us to more deeply examine affective correlates of exposure to architectural forms, especially when combined with eye-tracking and preference data. This study builds on earlier preference studies comparing seven pairs of traditional vs. modern civic buildings from the National Civic Art Society and an eye-tracking study with the same images from the Human Architecture and Planning Institute. It explores how metrics for engagement and positive emotional experience are correlated with exposure to traditional and modern forms. In agreement with other eye-tracking studies, as well as with the broader literature, the results indicate that traditional stimuli elicit positive affective responses, while modern stimuli result in negative responses across participants. Full article

Objective: Systemic lupus erythematosus (SLE) is a complex autoimmune disease, and its diagnosis can cause considerable anxiety and uncertainty for those affected. This study aimed to investigate the effect of a one-day educational seminar on disease-specific knowledge among patients with SLE. Additionally, the influence on subjective needs, the cognitive and emotional impact of the disease, and health-related lifestyle were examined. Methods: Patients were randomly assigned in a 1:1 ratio to an intervention group or a waiting list control group. Both groups attended the seminar. Disease-specific knowledge was measured using a multiple-choice questionnaire. The primary objective was the change in knowledge after the intervention. Results: Thirty-nine participants were included in the analysis. The mean score difference between the waiting list control group and the intervention group was 3.4 points out of a maximum of 20 (95% CI 1.8 to 5) immediately after the seminar and 1.6 (95% CI −0.6 to 3.5) three months later. Pooled data from both groups showed an increase in SLE-specific knowledge from 13.7 points to 17.3 points. Three months later, SLE-specific knowledge remained above the initial value at 15.4 points. However, no influence on lifestyle was observed. Conclusion: A one-day seminar can increase disease-specific knowledge and reduce unmet informational needs but does not lead to lifestyle modifications. Full article

The Fifth Industrial Revolution (Industry 5.0) foregrounds human–machine collaboration, sustainability, and resilience as organizing principles for next-generation cyber-physical systems. Yet the identity and access management (IAM) architectures inherited from Industry 4.0 remain perimeter-centric, policy-static, and blind to the behavioral dynamics of human–AI teaming. This paper introduces the Human-Centric Zero Trust Identity Architecture (HC-ZTIA), a novel framework that repositions identity as the adaptive control plane for Industry 5.0 environments. HC-ZTIA integrates three mutually reinforcing innovations: (1) a Joint Embedding Predictive Architecture (JEPA)-driven Behavioral Identity Assurance Engine (BIAE) that learns abstract world models of operator and machine-agent behavior to perform continuous, context-aware identity verification without relying on raw biometric surveillance; (2) a Privacy-Preserving Adaptive Authorization Protocol (PP-AAP) employing zero-knowledge proofs and federated policy evaluation to enforce least-privilege access across human, non-human, and hybrid identity classes while satisfying data-minimization mandates; and (3) a Resilience-Oriented Trust Degradation Model (RO-TDM) that provides formally verified fail-safe identity governance under adversarial, degraded, or disconnected operating conditions characteristic of operational technology (OT) and critical infrastructure. The framework is grounded in the Agile-Infused Design Science Research Methodology (A-DSRM) and formally extends National Institute of Standards and Technology (NIST) SP 800-207 and the Cybersecurity and Infrastructure Security Agency (CISA) Zero Trust Maturity Model by addressing five identified gaps in human-centric identity governance. Simulation results, validated through Monte Carlo trials with 95% confidence intervals, provide preliminary evidence that HC-ZTIA reduces identity-related breach exposure by 73.2% (±4.1%) while maintaining sub-200 ms authorization latency under the simulated conditions, offering a principled bridge between Zero Trust rigor and Industry 5.0 human-centricity. 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

Nanoparticles (NPs) have great potential for stimulating plant growth and development, reducing the negative impact of various types of stress on plants, and increasing the yield of agriculturally important crops. Metal oxide NPs (MONPs) have been shown to have a significant effect on the physiological and biochemical processes in plants, enhancing plant resilience. Among them, CuO, Mn_xO_x, and ZnO NPs are of particular interest because they contain elements essential for plant function. However, widespread use in agrochemistry and plant protection requires a preliminary risk assessment due to their potential phytotoxic effects. Phytotoxicity manifests through the development of oxidative stress, genotoxicity, and transcriptional disruption. A decrease in plant growth and photosynthesis, increased lipid peroxidation (LPO), and the accumulation of toxic NPs in plant tissues were also observed. Among the studied MONPs, CuO and ZnO NPs exhibit the greatest phytotoxic effects. However, the effects of MONPs are dose-dependent. Numerous studies have shown that MONPs can stimulate plant biometric parameters and productivity, as well as influence biochemical processes. MONPs have been shown to influence the functioning of the plant antioxidant system, manifested by modulating the content of reactive oxygen species (ROS), the activity of antioxidant enzymes (AOEs), and the regulation of signaling pathways mediated by ROS and reactive nitrogen species. Furthermore, MONPs influence the accumulation of proline and phenols in plant tissues. MONPs have a pronounced effect on the functioning of the plant photosynthetic apparatus, manifested by changes in pigment content, the activity of photosynthetic enzymes, and the functioning of photosystems. MONPs can improve nutrient absorption, regulate osmotic balance, and activate plant defense mechanisms. ZnO NPs are effective in mitigating salt stress. CuO and Mn_xO_x NPs have shown promise in mitigating biotic stress. Furthermore, these NPs were found to reduce the toxicity of heavy metals to plants. Overall, when used wisely, MONPs hold promise for enhancing the physiological, biochemical, and agronomic performance of crop plants under conditions of global climate change, effectively addressing food security issues. Full article

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