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Cultural heritage preservation increasingly relies on data-driven technologies, yet most existing systems lack the cognitive and temporal depth required to support meaningful, transparent, and policy-informed decision-making. This paper proposes a conceptual framework for memory-enabled, semantically grounded AI agents in the cultural domain, showing how the integration of the ICCROM/CCI ABC method for risk assessment into the Panoptes ontology enables the structured encoding of risk cognition over time. This structured risk memory becomes the foundation for agentic reasoning, supporting prioritization, justification, and long-term preservation planning. It is argued that this approach constitutes a principled step toward the development of Cultural Agentic AI: autonomous systems that remember, reason, and act in alignment with cultural values. Proof-of-concept simulations illustrate how memory-enabled agents can trace evolving risk patterns, trigger policy responses, and evaluate mitigation outcomes through structured, explainable reasoning.

Immersive technologies are revolutionary technological advancements that offer users unparalleled experiences of immersion in a virtual or mixed world of virtual and real elements. In such technology, user privacy, security, and anonymity are paramount, as users often share private and sensitive information. Therefore, user authentication is a critical requirement in these environments. This paper presents a systematic literature review of recently published research papers on immersive technology-based user authentication mechanisms. After conducting the literature search in September 2023 using Scopus, the selection process identified 36 research publications that were further analyzed. The analysis revealed three major types of authentications related to immersive technologies, consistent with previous works: knowledge-based, biometric, and multi-factor methods. The reviewed papers are categorized according to these groups, and the methods used are scrutinized. To the best of our knowledge, this systematic literature review is the first that provides a comprehensive consolidation of immersive technologies for user authentication in virtual, augmented, and mixed reality.

Background Incretin-based therapies are used in the treatment of type 2 diabetes mellitus (T2DM) and obesity. We investigated the changes in arterial stiffness and left ventricular (LV) myocardial deformation after 6-month treatment with the GLP-1 analogue liraglutide in subjects with newly diagnosed T2DM. Methods We randomized 60 patients with newly diagnosed and treatment-naive T2DM to receive either liraglutide (n = 30) or metformin (n = 30) for 6 months. We measured at baseline and after 6-month treatment: (a) carotid-femoral pulse wave velocity (PWV) (b) LV longitudinal strain (GLS), and strain rate (GLSR), peak twisting (pTw), peak twisting velocity (pTwVel) and peak untwisting velocity (pUtwVel) using speckle tracking echocardiography. LV untwisting was calculated as the percentage difference between peak twisting and untwisting at MVO (%dpTw–Utw MVO ), at peak (%dpTw–Utw PEF ) and end of early LV diastolic filling (%dpTw–Utw EDF ) (c) Flow mediated dilatation (FMD) of the brachial artery and percentage difference of FMD (FMD%) (d) malondialdehyde (MDA), protein carbonyls (PCs) and NT-proBNP. Results After 6-months treatment, subjects that received liraglutide presented with a reduced PWV (11.8 ± 2.5 vs. 10.3 ± 3.3 m/s), MDA (0.92 [0.45–2.45] vs. 0.68 [0.43–2.08] nM/L) and NT-proBNP (p < 0.05) in parallel with an increase in GLS (− 15.4 ± 3 vs. − 16.6 ± 2.7), GLSR (0.77 ± 0.2 vs. 0.89 ± 0.2), pUtwVel (− 97 ± 49 vs. − 112 ± 52°, p < 0.05), %dpTw–Utw MVO (31 ± 10 vs. 40 ± 14), %dpTw–Utw PEF (43 ± 19 vs. 53 ± 22) and FMD% (8.9 ± 3 vs. 13.2 ± 6, p < 0.01). There were no statistically significant differences of the measured markers in subjects that received metformin except for an improvement in FMD. In all subjects, PCs levels at baseline were negatively related to the difference of GLS (r = − 0.53) post-treatment and the difference of MDA was associated with the difference of PWV (r = 0.52) (p < 0.05 for all associations) after 6-month treatment. Conclusions Six-month treatment with liraglutide improves arterial stiffness, LV myocardial strain, LV twisting and untwisting and NT-proBNP by reducing oxidative stress in subjects with newly diagnosed T2DM. ClinicalTrials.gov Identifier NCT03010683

Backround Metabolic-dysfunction Associated Steatotic Liver Disease (MASLD) has been associated with increased cardiovascular risk. The aim of this Randomized Double-blind clinical Trial was to evaluate the effects of coenzyme-Q10 supplementation in patients with MASLD in terms of endothelial, vascular and myocardial function. Methods Sixty patients with MASLD were randomized to receive daily 240 mg of coenzyme-Q10 or placebo. At baseline and at 6-months, the a)Perfused boundary region of sublingual vessels using the Sideview Darkfield imaging technique, b)pulse-wave-velocity, c)flow-mediated dilation of the brachial artery, d)left ventricular global longitudinal strain, e)coronary flow reserve of the left anterior descending coronary artery and f)controlled attenuation parameter for the quantification of liver steatosis were evaluated. Results Six months post-treatment, patients under coenzyme-Q10 showed reduced Perfused boundary region (2.18 ± 0.23vs.2.29 ± 0.18 μm), pulse-wave-velocity (9.5 ± 2vs.10.2 ± 2.3 m/s), controlled attenuation parameter (280.9 ± 33.4vs.304.8 ± 37.4dB/m), and increased flow-mediated dilation (6.1 ± 3.8vs.4.3 ± 2.8%), global longitudinal strain (-19.6 ± 1.6vs.-18.8 ± 1.9%) and coronary flow reserve (3.1 ± 0.4vs.2.8 ± 0.4) compared to baseline ( p  < 0.05). The placebo group exhibited no improvement during the 6-month follow-up period ( p  > 0.05). In patients under coenzyme-Q10, the reduction in controlled attenuation parameter score was positively related to the reduction in Perfused boundary region and pulse wave velocity and reversely related to the increase in coronary flow reserve and flow-mediated dilation ( p  < 0.05 for all relations). Conclusions Six-month treatment with high-dose coenzyme-Q10 reduces liver steatosis and improves endothelial, vascular and left ventricle myocardial function in patients with MASLD, demonstrating significant improvements in micro- and macro-vasculature function. Trial Registration NCT05941910

This paper presents the design, development, and pilot deployment of Vitalia, an AR-gamified application targeting the formation of healthy habits in primary education children. Developed within the EU DUSE project, Vitalia integrates physical activity, nutritional education, and immersive storytelling into a gamified framework to promote sustained behavioral change. Grounded in evidence-based behavior change models and co-designed with health, nutrition, and physical activity experts, the system envisions high daily engagement rates and measurable knowledge improvements. The concept positions Vitalia as a scalable model for child-centric, ethically responsible digital health interventions, with the potential to be integrated into school curricula and public health strategies.

Black spot identification, a spatiotemporal phenomenon, involves analysing the geographical location and time-based occurrence of road accidents. Typically, this analysis examines specific locations on road networks during set time periods to pinpoint areas with a higher concentration of accidents, known as black spots. By evaluating these problem areas, researchers can uncover the underlying causes and reasons for increased collision rates, such as road design, traffic volume, driver behaviour, weather, and infrastructure. However, challenges in identifying black spots include limited data availability, data quality, and assessing contributing factors. Additionally, evolving road design, infrastructure, and vehicle safety technology can affect black spot analysis and determination. This study focused on traffic accidents in Greek road networks to recognize black spots, utilizing data from police and government-issued car crash reports. The study produced a publicly available dataset called Black Spots of North Greece (BSNG) and a highly accurate identification method.

Agricultural pollution models are a valuable tool for researchers and managers to predict and assess the potential contamination from the use of fertilizers and pesticides in the field. RZWQM2 is a comprehensive software package developed by the US EPA to predict environmental pollution after agrochemical application. The aim of the present study was to predict, using RZWQM2, the nitrogen and pesticides contents in soil of a monocrop and a tree-crop agroforestry system, and evaluate the effect of trees in reducing pollutants. Soil, weather, and agrochemical parameters for each setup were used as inputs in the model. Soil samples were collected at various depths and distances from the olive trees and were analyzed in the laboratory for nitrogen and pesticide contents. From the analysis of the results, it can be concluded that the model could identify the positive impact of the tree-crop agroforestry system in pollution reduction. Comparing the estimates with the relevant field data, the model presented some overestimation of the pesticide levels, particularly for the high-adsorptive and persistent pendimethalin herbicide, and slightly underestimated the concentrations of nitrates in the soil profile, while ammonium concentrations were well described. Overall, the model can be considered a useful and powerful tool for assessing the positive impacts of agroforestry systems in reducing soil pollution.

Background and Objectives: Glucagon-like peptide-1 receptor agonists (GLP-1RA) and sodium-glucose cotransporter-2 inhibitors (SGLT-2i) are cardioprotective drugs. We investigated their effects on left atrial function, a major determinant of cardiac diastolic dysfunction in type 2 diabetes mellitus. We also explored the association of changes in arterial stiffness with those of the LA strain after treatment. Materials and Methods: A total of 200 patients (59.5 ± 9.1 year old, 151 male) with type 2 diabetes mellitus treated with metformin were randomized to insulin (n = 50 served as controls), liraglutide (n = 50), empagliflozin (n = 50) or their combination (liraglutide + empagliflozin) (n = 50). We measured at baseline and 6 months post-treatment: (a) left atrial and global left ventricular longitudinal strain by speckle tracking echocardiography; (b) pulse wave velocity (PWV) and central systolic blood pressure. Results: At baseline, there was a correlation of the LA reservoir strain with PWV (r = −0.209, p = 0.008), central SBP (r = −0.151, p = 0.030), EF (r = 0.214, p = 0.004) and GLS (r = −0.279, p = 0.009). The LA reservoir change 6 months post-treatment was correlated with the PWV change in all groups (r = −0.242, p = 0.028). The LA reservoir change 6 months post-treatment was correlated with the GLS change in all groups (r = −0.322, p = 0.004). Six months after intervention, patients treated with liraglutide, empagliflozin and their combination improved the left atrial reservoir strain (GLP1RA 30.7 ± 9.3 vs. 33.9 ± 9.7%, p = 0.011, SGLT2i 30 ± 8.3 vs. 32.3 ± 7.3%, p = 0.04, GLP1&SGLT2i 29.1 ± 8.7 vs. 31.3 ± 8.2, p = 0.007) compared to those treated with insulin (33 ± 8.3% vs. 32.8 ± 7.4, p = 0.829). Also, patients treated with liraglutide and the combination liraglutide and empagliflozin had improved left atrial conduction strain (p < 0.05). Empagliflozin or the combination liraglutide and empagliflozin showed a greater decrease of PWV and central and brachial systolic blood pressure than insulin or GLP-1RA. (p < 0.05). Conclusions: Impaired aortic elastic properties are associated with a decreased LA strain in type 2 diabetics. Treatment with liraglutide, empagliflozin and their combination for 6 months showed a greater improvement of left atrial function compared to insulin treatment in parallel with the improvement of arterial and myocardial functions.

Identifying accidents in road black spots is crucial for improving road safety. Traditional methodologies, although insightful, often struggle with the complexities of imbalanced datasets, while machine learning (ML) techniques have shown promise, our previous work revealed that supervised learning (SL) methods face challenges in effectively distinguishing accidents that occur in black spots from those that do not. This paper introduces a novel approach that leverages positive-unlabeled (PU) learning, a technique we previously applied successfully in the domain of defect detection. The results of this work demonstrate a statistically significant improvement in key performance metrics, including accuracy, precision, recall, F1-score, and AUC, compared to SL methods. This study thus establishes PU learning as a more effective and robust approach for accident classification in black spots, particularly in scenarios with highly imbalanced datasets.

This paper introduces the concept of evolving actor–network ontologies (EANO) as a new paradigm for cultural digital twins. Building on actor–network theory, EANO reframes ontologies from static representations into reflexive, dynamic structures in which semantic interpretations are continuously negotiated among heterogeneous actors. We propose a five-layer architecture that operationalizes this principle, embedding reflexivity, actor salience, and systemic parameters such as resistance and volatility directly into the ontological model. To illustrate this approach, we present minimal simulations that demonstrate how different actor constellations and systemic conditions lead to distinct patterns of semantic evolution, ranging from expert erosion to contested equilibria and balanced coexistence. Rather than serving as predictive models, these simulations exemplify how EANO captures semantic plurality and contestation within a transparent and interpretable framework. The contribution of this work is thus twofold: it provides a conceptual foundation for evolving ontologies in digital heritage and a lightweight demonstration of how such models can be instantiated and explored computationally.