Search Results (401)

The increase in population and demand for the various needs of citizens increases the interaction with the geo-environment. Thus, the rate of natural events affecting daily human life increases. Such an event occurred on a rock cliff in a densely populated area in İstanbul (Türkiye). More than four rock blocks (approximately 3–5 m³) belonging to the Paleozoic sequence of İstanbul, composed of nodular limestone with sandy-clay interlayers, detached and fell. The blocks traveled along a path of approximately 60 m and stopped by crushing a couple of buildings downslope. The path was rough and contained various surface conditions (e.g., bedrock, talus, and plants). This study was initiated by the examination of the dimensions of failed rock blocks, their paths, and topographic conditions. Unmanned vehicles (drones) facilitated the generation of 3D numerical models of topographic changes on the site. Quantifying discontinuity properties (such as persistence, spacing, roughness, etc.) and defining weathering properties comprises the second stage, along with sampling. Based on digital topographic data and field observations, cross-sections were defined by means of possible rockfall areas within the area of potentially unstable blocks. Numerical analysis and rockfall analysis were conducted along these critical sections. Interpretation of laboratory data and results obtained from numerical studies leads to an understanding of the mechanism of the recent rockfall event and demonstrates the most critical areas to be considered and reinforced. The research comprises proposing appropriate reinforcement techniques due to the strong Turkish regulations along the “Bosphorus Waterfront Protected Zone”. The study advises pre-cleaning of potentially unstable blocks after a fence production on paths where rocks could fall, and rock anchors in some localities with varying lengths. The latest part of the research covers the re-assessment of mitigation processes with numerical models, which shows that the factor of safety increased to the desired levels. The reinforcement applications at the site match well with the proposed prevention methods. Full article

Background: Mechanical loading is a fundamental regulator of bone remodelling; however, the mechanotransduction mechanisms governing alveolar bone adaptation under tensile-dominant orthodontic loading remain insufficiently defined. In particular, the molecular pathways associated with tension-driven cortical modelling in the periodontal ligament (PDL)–bone complex have not been systematically interpreted in the context of advanced biomechanical simulations. Methods: A nonlinear finite element model of the alveolar bone–PDL–tooth complex was developed using patient-specific CBCT data. Three loading configurations were analysed: (i) conventional orthodontic loading, (ii) loading combined with corticotomy alone, and (iii) a translation-dominant configuration generated by the Bone Protection System (BPS). Pressure distribution, displacement vectors, and stress polarity within the PDL and cortical plate were quantified across different bone density conditions. The mechanical outputs were subsequently interpreted in relation to established mechanotransductive molecular pathways involved in osteogenesis and angiogenesis. Results: Conventional loading generated compression-dominant stress fields within the marginal PDL, frequently exceeding physiological thresholds and producing moment-driven root displacement. Corticotomy alone reduced local stiffness but did not substantially alter stress polarity. The BPS configuration redirected loads toward a tensile-favourable mechanical environment characterised by reduced peak compressive pressures and parallel (translation-dominant) displacement vectors. The predicted tensile stress distribution is compatible with activation profiles of key mechanosensitive pathways, including integrin–FAK signalling, Wnt/β-catenin–mediated osteogenic differentiation and HIF-1α/VEGF-driven angiogenic coupling, suggesting a microenvironment that may be more conducive to cortical apposition than to resorption. Conclusions: This study presents a computational–molecular framework linking finite element–derived tensile stress patterns with osteogenic and angiogenic signalling pathways relevant to alveolar bone remodelling. The findings suggestthat controlled redirection of orthodontic loading toward tensile domains may shift the mechanical environment of the PDL–bone complex toward conditions associated with osteogenic than resorptive responses providing a mechanistic basis for tension-induced cortical modelling. This mechanobiological paradigm advances the understanding of load-guided alveolar bone adaptation at both the tissue and molecular levels. Full article

Data governance for responsible AI systems remains challenged by the lack of automated tools that can apply robust privacy-preserving techniques without destroying analytical value. We propose AnonymAI, a novel methodological framework that integrates LLM-based intelligent agents, the mathematical guarantees of differential privacy, and an automated workflow to generate anonymized datasets for analytical applications. This framework produces data tables with formally verifiable privacy protection, dramatically reducing the need for manual classification and the risk of human error. Focusing on the protection of tabular data containing sensitive personal information, AnonymAI is designed as a generalized, replicable pipeline adaptable to different regulations (e.g., General Data Protection Regulation) and use-case scenarios. The novelty lies in combining the contextual classification capabilities of LLMs with the mathematical rigor of differential privacy, enabling an end-to-end pipeline from raw data to a protected, analysis-ready dataset. The efficiency and formal guarantees of this approach offer significant advantages over conventional anonymization methods, which are often manual, inconsistent, and lack the verifiable protections of differential privacy. Validation studies, covering both controlled experiments on four types of synthetic datasets and broader tests on 19 real-world public tables from various domains, confirmed the applicability of the framework, with the agent-based classifier achieving high overall accuracy in identifying confidential columns. The results demonstrate that the protected data maintains high value for statistical analysis and machine learning models, highlighting AnonymAI’s potential to advance responsible data sharing. This work paves the way for trustworthy and scalable data governance in AI through a rigorously engineered automated anonymization pipeline. Full article

Industry 5.0 challenges higher education to adopt human-centred and sustainable uses of artificial intelligence, yet many current deployments still treat generative AI as a stand-alone tool, neurophysiological sensing as largely laboratory-bound, and governance as an external add-on rather than a design constraint. This article introduces Nested Learning as a neuro-adaptive ecosystem design in which generative-AI agents, IoT infrastructures and multimodal deep learning orchestrate instructional support while preserving student agency and a “pedagogy of hope”. We report an exploratory two-phase mixed-methods study as an initial empirical illustration. First, a neuro-experimental calibration with 18 undergraduate students used mobile EEG while they interacted with ChatGPT in problem-solving tasks structured as challenge–support–reflection micro-cycles. Second, a field implementation at a university in Madrid involved 380 participants (300 students and 80 lecturers), embedding the Nested Learning ecosystem into regular courses. Data sources included EEG (P300) signals, interaction logs, self-report measures of engagement, self-regulated learning and cognitive safety (with strong internal consistency; $\alpha /\omega \ge 0.82$), and open-ended responses capturing emotional experience and ethical concerns. In Phase 1, P300 dynamics aligned with key instructional micro-events, providing feasibility evidence that low-cost neuro-adaptive pipelines can be sensitive to pedagogical flow in ecologically relevant tasks. In Phase 2, participants reported high levels of perceived nested support and cognitive safety, and observed associations between perceived Nested Learning, perceived neuro-adaptive adjustments, engagement and self-regulation were moderate to strong ($r=0.41$–$0.63$, $p<0.001$). Qualitative data converged on themes of clarity, adaptive support and non-punitive error culture, alongside recurring concerns about privacy and cognitive sovereignty. We argue that, under robust ethical, data-protection and sustainability-by-design constraints, Nested Learning can strengthen academic resilience, learner autonomy and human-centred uses of AI in higher education. Full article

Free live sports streaming (FLS) services attract millions of users who, driven by the excitement of live events, often engage with these high-risk platforms. Although these platforms are widely perceived as risky, the specific threats they pose have lacked large-scale empirical analysis. This paper addresses this gap through a comprehensive study of the FLS ecosystem, conducted during two major international sporting events (UCL playoffs and NHL Stanley Cup Playoffs, 2024–2025 season). We analyze the infrastructure, security threats, and privacy violations that define this space. Analysis of 260 unique domains uncovers systemic security risks, including drive-by downloads delivering persistent malware, and widespread privacy violations, such as invasive device fingerprinting that disregards regulations like the General Data Protection Regulation (GDPR). Furthermore, we map the ecosystem’s resilient infrastructure, identifying eight clusters of co-owned domains. These findings imply that effective countermeasures must target the centralized infrastructure and ephemeral nature of the FLS ecosystem beyond traditional blocking. Full article

Wearable technologies are increasingly integrated into digital health systems to support continuous remote monitoring in oncology; however, the lack of standardized and reproducible criteria for device selection limits their scalable and regulation-compliant adoption in clinically oriented infrastructures. This study proposes a preclinical benchmarking framework for the systematic evaluation of commercially available wearable devices for oncology applications. Devices were assessed across six predefined dimensions: biometric data acquisition, application programming interface-based interoperability, regulatory compliance, battery autonomy, cost, and absence of mandatory subscription fees. From an initial pool of 23 devices, a stepwise screening process identified 6 eligible wearables, which were compared using a semi-quantitative weighted scoring system. The benchmarking analysis identified the Withings ScanWatch 2 as the highest-ranked device, achieving a score of 37/40 and representing the only solution combining medical-grade certification for selected functions, extended battery life (up to 30 days), declared General Data Protection Regulation-compliant data governance, and fully accessible application programming interfaces. The remaining devices scored between 17 and 23 due to limitations in certification, battery autonomy, or data accessibility. This work introduces a reproducible preclinical benchmarking methodology that supports transparent wearable device selection in oncology and provides a foundation for future scalable digital health integration under appropriate regulatory and interoperability governance. Full article

Background: Schools play a central role in child development and socialization and can function as protective environments that mitigate the effects of adversity. Building on the Social Ecological Model and Community School Transformation, we propose a “Schools-as-Neighborhoods” framework that conceptualizes schools as intentionally designed microenvironments capable of generating social capital, promoting positive childhood experiences, and buffering harmful neighborhood exposures through trauma-informed programming. Methods: We conducted a convergent mixed-methods study across four public and charter schools in Milwaukee, Wisconsin, serving grades five through nine. STRYV365’s peak team and Brain Agents gamified intervention were implemented between 2022–2024. Quantitative surveys and qualitative data assessed students’ lived experiences, exposure to adversity, emotional awareness, coping skills, and school connectedness/climate across multiple waves. Results: Across the four schools (n = 1626 students), baseline academic proficiency was low, and exposure to adversity was high among surveyed participants (n = 321), including bereavement (74%) and family incarceration (56%). Despite these challenges, qualitative findings revealed strengthened emotional regulation, empathy, motivation, and goal setting among students engaged in trauma-informed programming. Teachers reported improved peer interaction and community building during sustained implementation. Conclusion: The Schools-as-Neighborhoods framework highlights the value of trauma-informed, relationship-centered school environments in promoting student well-being. By positioning schools as cohesive ecosystems that foster belonging and cultivate social capital, this approach offers educators and policymakers a pathway for mitigating the effects of hostile lived environments and supporting students’ mental health, social development, and engagement in learning. Full article

Explainability is increasingly expected to support not only interpretation, but also accountability, human oversight, and auditability in high-risk Artificial Intelligence (AI) systems. However, in many deployments, explanations are generated as isolated technical reports, remaining weakly connected to decision provenance, governance actions, audit logs, and regulatory documentation. This short communication introduces XAI-Compliance-by-Design, a modular engineering framework for explainable artificial intelligence (XAI) systems that routes explainability outputs and related technical traces into structured, audit-ready evidence throughout the AI lifecycle, designed to align with key obligations under the European Union Artificial Intelligence Act (EU AI Act) and the General Data Protection Regulation (GDPR). The framework specifies (i) a modular architecture that separates technical evidence generation from governance consumption through explicit interface points for emitting, storing, and querying evidence, and (ii) a Technical–Regulatory Correspondence Matrix—a mapping table linking regulatory anchors to concrete evidence artefacts and governance triggers. As this communication does not report measured results, it also introduces an Evidence-by-Design evaluation protocol defining measurable indicators, baseline configurations, and required artefacts to enable reproducible empirical validation in future work. Overall, the contribution is a practical blueprint that clarifies what evidence must be produced, where it is generated in the pipeline, and how it supports continuous compliance and auditability efforts without relying on post hoc explanations. Full article

Yersinia pestis caused the three plague pandemics that claimed more than two hundred million human lives. There is still no vaccine that meets all WHO requirements, and many researchers continue to develop plague vaccines using various technological platforms. For example, researchers led by Roy Curtiss 3rd have developed a new approach to achieve controlled, delayed attenuation of bacterial pathogens. Mutants generated using this method were superior in protecting Y. pestis-infected mice immunized with strains generated using traditional gene knockout. However, further studies are needed to determine the safety and efficacy of these delayed-attenuated strains in other mammalian species in order to extrapolate on humans the data obtained in accordance with the FDA Animal Rule. Three Y. pestis strains, a Δcrp mutant, a mutant with arabinose-dependent regulated crp expression (araC P_BAD crp) or an araC P_BAD crp mutant cured of plasmid pPst were derived from virulent wild-type strain 231. To evaluate the safety, outbred mice or guinea pigs were immunized subcutaneously with serial tenfold dilutions of mutated strains. For vaccine studies, immunized animals were subcutaneously challenged with 200 LD₁₀₀ (lethal dose in all exposed subjects) of the wild-type Y. pestis strain. The challenge caused the death of 100% of naïve animals in controls. The Y. pestis strain 231Δcrp was nonlethal in mice at a dose of 10⁷ CFs. The LD₅₀ of the 231Δcrp strain in guinea pigs increased by at least 10⁷-fold compared to that of the wild-type strain. The LD₅₀s of the 231P_BAD-crp mutant in mice and guinea pigs were approximately 10⁴-fold and 10⁷-fold higher than those of Y. pestis 231, respectively. The 231P_BAD-crp(pPst¯) strain did not cause death in mice (LD₅₀ > 10⁷ CFU) and guinea pigs (LD₅₀ > 10⁹ CFU) when administered subcutaneously and was capable of inducing intense protective immunity in both species of laboratory animals. Our research has shown once again the necessity of balance between safety and effectiveness demonstrating the feasibility of further investigation of crp mutants as promising candidate plague vaccines. Full article

Wetlands store large amounts of soil organic carbon stock (SOCS), making them crucial for global climate regulation. However, climate change, poor management, and weak protection policies threaten these stocks. To assess the contribution of different wetland types for national and international climate targets and to monitor the effectiveness of protection measures, additional research is required. Therefore, we assessed SOCS and disturbances from climate change, land use/land cover (LULC), and soil chemical composition in saline and eutrophic Ramsar sites in Serbia. Analyzing a total of 96 samples, we accounted for soil depth, reference soil group (RSG), and habitat/vegetation type. Mean SOCS in the saline site ranged from approximately 36 t·ha⁻¹ at 0–30 cm to 26 t·ha⁻¹ at 30–60 cm, whereas values were much higher for the eutrophic sites, ranging from 81 to 82 t·ha⁻¹ at 0–30 cm and 47–63 t·ha⁻¹ at 30–60 cm. Differences between groups for the whole soil columns (0–60 cm) were significant at the 0.1% level. While SOCS generally decreases with depth, it showed notable local variability, including occasional instances at deeper layers, indicating complex environmental and anthropogenic influences. Spatial mapping of soil chemistry parameters (pH, humus, P₂O₅, and K₂O) along with land use/land cover (LULC) data revealed nutrient dynamics influenced by agricultural activities. An analysis of regional climate data revealed temperature increases relative to the reference period of 1971–2000 by 0.5 °C for the decade 2001–2010 and of 1.5 °C for 2011–2020. Climate projections under the RCP4.5 and 8.5 scenarios predict further warming trends, as well as increased rainfall variability and drought risks. The results of our study contribute to quantifying the important, though variable, contribution of wetland sites to global climate regulation and show the influence of geogenic, pedogenic, and anthropogenic factors on SOCS. National policies should be adapted to safeguard these stocks and to limit negative effects from surrounding agricultural areas, as well as to develop strategies to cope with expected regional climate change effects. Full article

Background: Emotional intelligence (EI), particularly the ability to regulate one’s emotions, is a key protective factor against stress and burnout in high-demand occupations, including leadership and healthcare. Compassion and gratitude practices have been proposed as brief, scalable methods to strengthen emotion regulation, yet empirical evidence from randomised controlled trials remains limited. Objective: This study evaluated whether a four-week, self-directed online programme combining daily loving-kindness meditation and gratitude journaling improves EI among leaders. Methods: Forty-five leaders in Germany from diverse occupational sectors were recruited via LinkedIn and Xing and were randomised using a computer-generated random sequence to an intervention or wait-list control group. EI was measured pre- and post-intervention with the Emotional Competence Questionnaire (EKF), comprising recognising one’s own feelings (RU), recognising others’ feelings (RO), regulating one’s own feelings (RC; primary outcome), and expressing feelings (RE). Adherence was reported in categorical form (e.g., daily, 3–5×/week, 1–2×/week). Treatment effects were tested using mixed-design ANOVAs. Results: A significant Group × Time interaction emerged for emotion regulation (RC), indicating greater improvement in the intervention group compared with the control group. No significant interaction effects were found for RU, RO, or RE. Adherence data did not permit dose–response analysis. Conclusions: A brief, self-directed online compassion and gratitude programme selectively improved emotion regulation—the EI facet most strongly linked to stress buffering and resilience. Although effects did not extend to other EI dimensions, findings suggest that low-threshold digital practices may strengthen a core emotional skill relevant to psychological well-being in leadership roles. Because the sample did not primarily comprise healthcare professionals, implications for healthcare settings re-main conceptual; targeted trials in clinical populations are warranted. Full article

Healthcare faces a critical challenge: protecting sensitive medical data while enabling necessary clinical access. Evolving user behaviors, dynamic clinical contexts, and strict regulatory requirements demand adaptive access control mechanisms. Despite strict regulations, healthcare remains the most breached industry, consistently facing severe security risks related to unauthorized access. Traditional access control models cannot handle contextual variations, detect credential compromise, or provide transparent decision rationales. To address this, SAFE-GUARD (Semantic Access Control Framework Employing Generative User Assessment and Rule Decisions) is proposed as a two-layer framework that combines behavioral analysis with policy enforcement. The Behavioral Analysis Layer uses Retrieval-Augmented Generation (RAG) to detect contextual anomalies by comparing current requests against historical patterns. The Rule-Based Policy Evaluation Layer independently validates organizational procedures and regulatory requirements. Access is granted only when behavioral consistency and both organizational and regulatory policies are satisfied. We evaluate SAFE-GUARD using simulated healthcare scenarios with three LLMs (GPT-4o, Claude 3.5 Sonnet, and Gemini 2.5 Flash) achieving an anomaly detection accuracy of 95.2%, 94.1%, and 91.3%, respectively. The framework effectively identifies both compromised credentials and insider misuse by detecting deviations from established behavioral patterns, significantly outperforming conventional RBAC and ABAC approaches that rely solely on static rules. Full article

Digital Twins are becoming central enablers of Europe’s digital and green transitions, yet their data-intensive and autonomous nature exposes them to one of the most complex regulatory environments in the world. This article presents a comprehensive scoping review of how six principal European digital laws—the General Data Protection Regulation, Data Governance Act, Data Act, Artificial Intelligence Act, NIS2 Directive, and Cyber Resilience Act—jointly govern the design, deployment, and operation of Digital Twin systems. Building on the PRISMA-ScR methodology, the study constructs a Unified Digital Twin Compliance Framework (UDTCF) that consolidates overlapping obligations across data governance, privacy, cybersecurity, transparency, interoperability, and ethical responsibility. The framework is operationalised through a Digital Twin Compliance Evaluation Matrix (DTCEM) that enables qualitative assessment of compliance maturity in research and innovation projects. Applying these tools to representative European cases in Smart Cities, Industrial Manufacturing, Transportation, and Energy Systems reveals strong convergence in data governance, security, and interoperability, but also persistent gaps in the transparency, explainability, and accountability of AI-driven components. The findings demonstrate that European digital legislation forms a coherent yet fragmented ecosystem that increasingly requires integration through compliance-by-design methodologies. The article concludes that Digital Twins can act not only as regulated technologies but also as compliance infrastructures themselves, embedding legal, ethical, and technical safeguards that reinforce Europe’s vision for trustworthy, resilient, and human-centric digital transformation. Full article

Ensuring product quality and safety is fundamental to sustainable production and consumption. With the rapid advancement of digital technologies such as blockchain and big data, quality and safety traceability systems have become essential tools to enhance transparency, accountability, and governance efficiency across supply chains. The sustainable functioning of these systems, however, depends on the coordinated actions of multiple stakeholders—including governments, enterprises, consumers, and industry associations—making the study of technological and institutional interactions particularly significant. This paper extends evolutionary game theory to the context of technology-enabled sustainable governance by constructing a tripartite game model involving government regulators, traceability enterprises, and consumers from both technological and institutional perspectives. Unlike existing studies, which focused solely on government regulation, this research explicitly incorporates the role of industry associations in shaping stakeholder behavior and integrates consumer rights protection mechanisms as well as the adoption of emerging technologies such as blockchain into the model. Analytical derivations and MATLAB-based simulations reveal that strengthening reward–penalty mechanisms and improving digital maturity significantly enhance enterprises’ incentives for truthful information disclosure; consumers’ verification and reporting behaviors generate bottom-up pressure that encourages stricter governmental supervision; and active participation of industry associations helps share regulatory costs and stabilize cooperative equilibria. These findings suggest that combining technological innovation with institutional collaboration not only improves transparency and strengthens consumer trust but also reshapes the incentive structures underlying traceability governance. The study provides new insights into how multi-stakeholder coordination and technological adoption jointly foster transparent, credible, and resilient traceability systems, offering practical implications for advancing digital transformation and co-governance in sustainable supply chains. Full article

Privacy enhancement technologies are significant in the development of digital payment systems. At present, multiple innovative digital payment solutions have been introduced and may be implemented globally soon. As cyber threats continue to increase in complexity, security is a crucial factor to consider before adopting any technology. In addition to prioritizing security in the development of digital payment systems, it is essential to address user privacy concerns. Modern digital payment solutions offer numerous advantages over traditional systems; however, they also introduce new considerations that must be accounted for during implementation. These considerations go beyond legislative requirements and encompass new payment methods, including transactions made through mobile devices regardless of internet connectivity. A range of regulations and guidelines exist to ensure user privacy in financial transactions, with the General Data Protection Regulation (GDPR) being particularly notable, while technical reports have thoroughly examined the differences between various privacy-enhancing technologies. Additionally, it is important to note that all legal payment systems are required to maintain information for audit purposes. This paper introduces a comprehensive framework that integrates all critical considerations for selecting appropriate privacy enhancement technologies within digital payment systems, while it utilizes a detailed scoring system designed for convenience and adaptability, allowing it to be employed for purposes such as auditing. Thus, the proposed scoring framework integrates security, GDPR compliance, audit, privacy-preserving technical measures, and operational constraints to assess privacy technologies for digital payments. Full article