Search Results (404)

Sustainable urban development increasingly relies on hyperlocal environmental analytics created by smart city platforms that combine stationary and mobile sensors, Earth observations, meteorology, and land-use data. However, accurate spatio-temporal resolution can provide indirect identification and amplify cybersecurity threats. This article proposes the regulatory and technical mapping that implements the General Data Protection Regulation (GDPR) and the Network and Information Security Directive (NIS2) throughout the lifecycle of environmental data—reception, transport, storage, analytics, sharing, and publication. The methods combine doctrinal legal analysis, a review of the scope of recent research, formalized compliance modeling, modeling with synthetic city-scale datasets, expert identification, and demonstration of integrated analytics. The demonstration links deep evaluation of neural abnormalities (convolutional plus recurrent layers), short-term Fourier transformation of sensor signals, byte-to-image telemetry fingerprints, and protocol event counters, thereby tracking detection to explanatory evidence and to control actions. Deliverables include a matrix aligning lifecycle stages with GDPR principles and rights, as well as with the responsibilities of NIS2; a checklist for assessing the impact on data protection, which takes into account the risks of fairness and stigmatization; a basic set of controls for identification and access, secure design, monitoring, continuity, supplier assurance, and incident reporting; as well as a multi-layered publishing strategy that combines transparency with privacy through aggregation, delayed release, differentiated privacy budgets, and research enclaves. The visualization confirms that technical signals can be included in audit-ready reporting and automated response, while the guidelines legally clarify the relevant bases for common use cases such as air quality assurance networks, noise mapping, citizen sensor applications, and mobility and exposure modeling. The effects of the policy emphasize shared services for small municipalities, supply chain security, and ongoing review to counteract the mosaic effect. Overall, the study shows how cities can maximize environmental and social value based on environmental data, while maintaining privacy, sustainability, and equity by design. Full article

Infrasound, physically defined as sound at frequencies below 20 Hertz, can travel long distances with minimal attenuation and permeate biological tissues due to its marked particle displacement and deep penetration. Generated by both natural phenomena and human-made systems, infrasound has drawn increasing scientific and public attention regarding its potential physiological and psychological effects. Experimental studies demonstrate that infrasound can modulate mechanosensitive structures at the cellular level, particularly pressure-sensitive ion channels such as PIEZO1 and TRPV4, leading to intracellular calcium influx, oxidative stress, altered intercellular communication, and in some settings, apoptosis. These responses vary according to sound pressure levels, frequencies, exposure duration, and tissue type. In the cardiovascular system, higher sound pressures have been associated with mitochondrial injury and fibrosis, whereas low sound pressures may exert context-dependent protective effects. In animal models, prolonged or intense exposure to infrasound has been shown to induce neuroinflammatory responses and memory impairment. Short-term studies in humans at moderate intensities have reported minimal physiological changes, with psychological and contextual factors influencing symptom perception. Occupational environments such as factories and agricultural settings may contain elevated levels of infrasound, underscoring the importance of systematic measurements and exposure assessments. At the same time, controlled infrasound stimulation has shown potential as an adjunct modality in bone repair and tissue regeneration, highlighting its dual capacity as both a biological stressor and a possible therapeutic tool. Overall, existing data indicate that infrasound may be harmful at chronic exposure depending on intensity and frequency, yet beneficial when precisely regulated. Future research should standardize exposure metrics, refine measurement technologies, and clarify dose–response relationships to better define the health risks and therapeutic applications of infrasound. Full article

Phacoemulsification is performed within a highly dynamic intraocular environment, in which fluid exchange, pressure regulation, and tissue biomechanics interact continuously. Although modern cataract surgery is considered safe and efficient, disruption of this delicate intraoperative microenvironment remains a major source of complications. Among fluidics-related events, post-occlusion break surge represents one of the most critical destabilizing factors of the anterior chamber. A surge occurs when the sudden release of an occluded aspiration port generates an abrupt pressure–volume imbalance that cannot be immediately compensated by infusion, leading to a transient collapse of the intraocular environment. This narrative review integrates current experimental and clinical evidence on the pathophysiology, quantification, and technological control of surge, framing it as a model of environmentally driven intraoperative stress. The evolution of phacoemulsification fluidics, from gravity-based systems to active, adaptive, and predictive platforms, is analyzed in relation to their ability to preserve a stable and physiologic intraocular environment. Comparative data from contemporary devices are reviewed, highlighting differences in surge volume, recovery time, and pressure restitution. Special emphasis is placed on the impact of surge on the microenvironments of both the anterior and posterior segments, including endothelial stress, capsular instability, vitreoretinal traction, and macular perfusion. Emerging strategies such as handpiece-integrated pressure sensors, predictive fluidics algorithms, intraoperative imaging, and artificial intelligence are reshaping environmental control during surgery. Despite substantial technological progress, the complete elimination of surge remains an unmet need. Continued innovation, standardized biomechanical models, and robust clinical validation will be essential to further protect the intraoperative intraocular environment and improve long-term visual outcomes. Full article

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