Search Results (6,022)

Deep learning is increasingly integrated into oral rehabilitation workflows, particularly in implant planning, prosthodontic design automation, and peri-implant diagnosis. However, reported performance is heterogeneous and difficult to compare across tasks, modalities, and validation designs. The goal of this study was to critically analyze deep learning architecture families applied to oral rehabilitation and to provide task-driven selection guidance supported by an evidence table reporting dataset characteristics, validation strategy, and performance metrics. A focused narrative review was conducted using transparent, database-specific search criteria (final n = 10 included studies), emphasizing implant planning (cone–beam computed tomography [CBCT]-based segmentation), prosthodontic design (intraoral scan [IOS]/mesh inputs), and peri-implant diagnosis (periapical/panoramic radiographs). Evidence certainty for each clinical task was assessed using GRADE-informed ratings (High/Moderate/Low/Very Low). Extracted variables included clinical task, imaging modality, dataset size, architecture, validation strategy (internal vs. internal + external), split level, ground truth protocol, and performance metrics. A structured computational and hardware feasibility analysis was conducted for each architecture family to support real-world deployment planning. Encoder–decoder networks (U-Net/nnU-Net) dominate CBCT segmentation for implant planning, while detection architectures (Faster R-CNN, YOLO) support implant localization and peri-implant assessment on radiographs. Generative models (3D GANs, transformer-based point-to-mesh networks) enable crown design from three-dimensional scans. Hybrid CNN–Transformer architectures show promise for multimodal CBCT–IOS fusion, though direct evidence from the included studies remains limited to a single study. External validation remains uncommon yet essential given the risk of domain shift. In conclusion, architecture selection should be anchored to task geometry (2D vs. 3D), artifact burden, and required clinical output type. Reporting standards should prioritize dataset transparency, validation rigor, multi-center external testing, and uncertainty-aware outputs. Full article

Olive cultivation represents a key pillar of rural economies and cultural heritage in Mediterranean regions, including western Greece. Despite its socio-economic importance, the sector faces increasing pressures from climate change, market volatility, and technological transformation, while progress toward environmentally sustainable production remains uneven. This study investigates how olive farmers’ perceptions of carbon footprint and climate risks are influenced by their demographic characteristics. Primary data were collected through 402 structured questionnaires distributed to olive producers in the Aetolia–Acarnania region. The sample was designed to represent farmers directly engaged in olive production, ensuring the relevance and reliability of the collected data. The findings, based on descriptive statistics, reveal significant heterogeneity in producers’ perceptions of climate risks and their capacity to respond through sustainable practices. Demographic characteristics appear to play an important role in shaping awareness of carbon footprint and the potential adoption of environmentally responsible farming strategies. These results suggest that sustainability transitions in perennial cropping systems depend not only on technological availability but also on social, informational, and institutional capacities. Strengthening agricultural advisory services, farmer training, and climate adaptation strategies may therefore support the adoption of climate-smart practices in olive cultivation. Furthermore, cooperation and value-chain integration are identified as potentially important mechanisms for facilitating knowledge transfer and supporting the adoption of sustainable practices (e.g., efficient irrigation and optimized input use). However, their contribution to environmental performance and greenhouse gas mitigation cannot be directly inferred from the present perception-based analysis and should be examined in future research using appropriate quantitative or environmental assessment frameworks. Full article

Social determinants of health (SDoH) are non-medical factors shaped by the socioeconomic status of individuals or communities that influence the onset and progression of diseases and affect their outcomes. We have narratively analyzed the most important findings relating chronic kidney disease (CKD) and SDoH, evaluating the following items: (i) medical care and social determinants of health, (ii) socioeconomic risk for kidney disease at the individual level and (iii) socioeconomic risk for kidney disease at the population level. SDoH can be categorized by how they influence a person’s daily life. Individual factors include personal lifestyle choices such as smoking habits, alcohol consumption, and how a patient spends their non-working time. Community factors include structural elements such as average household income, educational attainment, employment rates, and the quality of the surrounding physical environment. Research consistently shows that a low socioeconomic status is a primary driver of poor clinical outcomes. While healthcare systems vary globally, the negative impact of socioeconomic deprivation on CKD patients remains a constant. Disadvantaged patients experience a faster loss of renal function, and there is a significantly higher incidence of cardiovascular events and mortality compared to those with financial stability. Financial hardship often leads to a “double burden,” where the struggle to afford care triggers a decline in both physical health and mental well-being. To improve patient care, it is essential to raise awareness among healthcare providers regarding the profound impact of these social factors. More precise data and thorough research are needed to fully understand these associations and develop targeted interventions. Full article

Generative Artificial Intelligence (GenAI) is transforming higher education in terms of pedagogy, student involvement, and academic management. This systematic literature review examines 30 peer-reviewed articles published from 2019 to 2025, adhering to PRISMA 2020 and Kitchenham’s methodologies. Descriptive and thematic analyses highlight five opportunities: (a) tailored and adaptive education; (b) deliberate fostering of critical thinking; (c) enhanced accessibility for varied learners; (d) teaching innovation via multimodal content development and feedback; and (e) collaborative methods that regard AI as a co-teacher. Four ongoing challenge categories also surface: (a) risks to academic integrity; (b) excessive dependence on GenAI that may hinder learner independence; (c) inconsistent faculty preparedness and change-management abilities; and (d) differences in infrastructure and policy both regionally and globally. Intersecting ethical issues, such as data privacy, algorithmic bias, transparency, and accountability, highlight the necessity for governance that aligns with institutional risk and reflects societal values. Analyzing the recent literature, this systematic review offers four contributions: (a) a recommendation model for responsible GenAI implementation in higher education institutions; (b) a framework for sustainable integration of GenAI; (c) a highlight of the future research recommendations; and (d) an integrated policy and pedagogical recommendations roadmap. These models emphasize the integration of AI literacy, ethical considerations, and critical thinking goals into educational programs. The review advocates for a strategic, stakeholder-focused approach to implementation that enhances rather than replaces human instruction, thus connecting GenAI’s educational potential with ethical, context-aware avenues for institutional transformation. Full article

Background/Objectives: Antimicrobial resistance (AMR) is a critical global public health challenge driven by inappropriate and excessive antimicrobial use (AMU) across human, animal, and environmental sectors. Method: This narrative review synthesizes recent evidence on antimicrobial utilization and resistance patterns. A structured search of PubMed, Scopus, and Web of Science was conducted for studies published between 2015 and 2025. Eligible sources included surveillance reports, registry-based analyses, and clinical studies. Data were qualitatively analyzed to identify key trends and regional variations. Result: Marked geographical variation in AMR was observed. Carbapenem resistance in Escherichia coli remains low globally (2–3%) but is higher in Southeast Asia (17–18%) and India (~40%). Klebsiella pneumoniae shows consistently high resistance (>40% globally; ~54% in India), while Pseudomonas aeruginosa exhibits stable resistance levels (35–45%). Resistance prevalence increases from primary to tertiary care settings, reflecting greater antimicrobial exposure. Vulnerable populations—including pediatric, elderly, pregnant, and immunocompromised patients—face higher risks of antimicrobial exposure and adverse outcomes, including nephrotoxicity, hepatotoxicity, and microbiome disruption. WHO AWaRe data indicate a global shift toward increased use of Watch-category antibiotics. Stewardship interventions, such as audit and feedback, prescribing restrictions, rapid diagnostics, and decision support systems, effectively reduce inappropriate AMU. Conclusions: Integrated, data-driven antimicrobial stewardship and robust surveillance systems are essential to mitigate the global burden of AMR. Full article

Reservoir release induced flooding is a major downstream hazard worldwide, yet most warning systems rely on hydraulic modeling and underuse real time reservoir operation data. This study presents a data driven framework to detect flood discharge events, assess downstream operational severity, and forecast daily discharges using deep learning. The approach was validated at the Ba Ha hydropower reservoir (Vietnam) with inflow, discharge, water level, and CHIRPS rainfall data to represent basin-scale precipitation forcing. More than 160 discharge events were identified using a composite Operational Severity Index (OSI) based on peak discharge, duration, and rise rate; although only ~2% were extreme, they posed the greatest risks. Among three Transformer-based models, Informer achieved the best short-term forecasting performance (RMSE ≈ 78 m³/s, R² ≈ 0.80), while Autoformer showed greater stability at longer horizons (3–7 days). In contrast, all models exhibited reduced skill under abrupt and extreme discharge conditions. These results demonstrate that combining trend and anomaly-aware modeling enables reliable discharge prediction and severity assessment without complex hydraulic simulations. The proposed framework provides a practical foundation for reservoir early warning systems by transforming routine operational data into actionable flood-risk information. Full article

Highway ramp merging requires autonomous vehicles to make safe and efficient decisions in dense mixed traffic, where strong vehicle interactions and rapidly changing acceptable gaps make the task particularly challenging. Existing reinforcement learning methods are often unimodal and overly conservative, while diffusion-based policies, despite their ability to generate multimodal actions, usually suffer from high inference latency and safety risks caused by unconstrained sampling. To address these issues, this paper proposes AM-DIMPO, an action-mask-guided safe diffusion-implicit policy optimization framework for ramp-merging tasks. The proposed method combines DDIM-based implicit sampling with a state-dependent continuous action mask to improve multimodal action generation efficiency while enhancing action feasibility. In addition, the mask correction signal is incorporated into policy learning to encourage the policy to generate actions closer to the safe feasible region. Experiments are conducted in a Gym-based ramp-merging simulator under both light-traffic and dense-traffic scenarios, where the proposed method is compared with classical reinforcement learning baselines, diffusion reinforcement learning baselines, and a safety-aware PPO baseline. The results show that, in dense traffic, AM-DIMPO achieves a merging success rate of 97.3%, an average speed of 16.27 m/s, and an inference latency of 68 ms; in light traffic, the success rate reaches 98.1%. Moreover, the proposed method maintains robust performance under the tested noisy-observation and reduced-visibility settings. Overall, AM-DIMPO achieves a favorable balance among empirical safety, traffic efficiency, robustness, and real-time inference performance in dense highway ramp-merging tasks. Full article

Fuel cell technologies are increasingly investigated as alternatives to conventional auxiliary diesel generators in order to enhance shipboard energy efficiency and reduce greenhouse gas emissions. This study presents a unified and uncertainty-driven system-level assessment of solid oxide fuel cell (SOFC) and proton exchange membrane fuel cell (PEMFC) systems operating as auxiliary power sources on a 200 m bulk carrier. Both technologies are evaluated under identical vessel characteristics, operating profiles, auxiliary load levels (360–600 kW), and cost assumptions, and are benchmarked directly against a conventional three–diesel-generator configuration. A modular numerical framework is developed to model propulsion–auxiliary interactions for ship speeds between 10 and 14 knots. SOFC systems are assessed using grey, bio-derived, and green natural gas pathways, while PEMFC systems are examined under grey, blue, and green hydrogen supply routes. Performance indicators include annual fuel consumption, carbon dioxide (CO₂) emission reduction, net present value (NPV), internal rate of return (IRR), payback period (PBP), and marginal abatement cost (MAC). Economic uncertainty is explicitly embedded in the framework through Monte Carlo simulation, where fuel prices (±20%) and capital costs are sampled across defined ranges, generating probabilistic distributions rather than single deterministic estimates. This uncertainty-centred approach enables assessment of robustness, downside risk, and probability of profitability. Results show that replacing a single operating 600 kW diesel generator with fuel cell systems reduces auxiliary fuel energy demand by 25–35% for SOFC and approximately 15–25% for PEMFC relative to the diesel benchmark. Annual CO₂ reductions range from 1.1 to 1.3 kt for SOFC systems and 1.8–2.8 kt for PEMFC configurations. Under grey fuel pathways, median NPVs reach approximately 2–4.5 M$ for SOFC and 9–17 M$ for PEMFC as load increases, with IRRs exceeding 15% and 30%, respectively. Transitional pathways exhibit narrower margins, while renewable pathways remain more sensitive to fuel price variability. The findings demonstrate that fuel pathway cost dominates lifecycle outcomes under uncertainty and that hydrogen-based PEMFC systems exhibit the strongest economic resilience within the examined market ranges. The framework provides structured, uncertainty-aware decision support and establishes a foundation for integration into model-based systems engineering (MBSE) environments for early stage ship energy system design. Full article

Accurate medium-horizon load forecasting and risk-aware unit commitment are critical for high-load power systems. This study develops an integrated prediction–optimization framework that couples 744 h recursive load forecasting with uncertainty-aware scheduling. In the forecasting stage, a CNN-LSTM model is tuned by the Dung Beetle Optimizer (DBO), while Monte Carlo Dropout is retained during inference to generate probabilistic trajectories and time-varying prediction intervals. In the scheduling stage, these forecast-derived intervals are embedded into a mixed-integer linear robust unit commitment model through a dynamic uncertainty budget. Using real-world load data from Southern China, the proposed method achieves average RMSE, MAE, MAPE, and R² values of 2941 kW, 2137 kW, 4.33%, and 0.97, respectively. Relative to SARIMA and Informer, the average RMSE is reduced by 48.1% and 26.0%, respectively, while point-forecasting performance remains competitive with XGBoost. The proposed model also provides the best overall interval quality, with average PINAW and Winkler Score values of 0.19 and 17,049, outperforming XGBoost, CNN-LSTM, and Informer. In the scheduling study, the proposed robust strategy reduces average EENS and LOLH to 68.6 kWh and 0.0454 h, respectively, and yields the lowest average generalized total cost of CNY 97.30 million, compared with 124.69 million CNY for the deterministic benchmark and CNY 99.66 million for the chance-constrained benchmark. These results show that forecast uncertainty can be effectively translated into more reliable and economical scheduling decisions. Full article

Background/Objectives: Prevention of virus-related cancers is a multifaceted process shaped by vaccination and public awareness. This study assessed awareness of virus–cancer relationships and willingness to vaccinate against a cancer-associated virus among medical and non-medical students. We also evaluated whether human papillomavirus (HPV)-vaccinated students demonstrate greater awareness of the HPV-cancer link compared to unvaccinated students, and examined willingness to vaccinate against a certain cancer-associated virus according to HPV vaccination status. Methods: This cross-sectional survey was conducted in Poland (October 2023–June 2024) and included 1013 first- and second-year university students recruited via convenience sampling. Participation was voluntary and anonymous. Results: Awareness of virus–cancer relationships was low, ranging from 19% for Epstein–Barr virus-related cancers to 43.8% for HPV-related cervical cancer. Women were more likely than men to recognize the HPV–cervical cancer link (OR = 2.08, p < 0.001), supporting gender differences and the need for gender-neutral HPV education with targeted strategies for men. Medical students demonstrated higher awareness than non-medical students. HPV vaccination coverage was low (14.5%), with higher uptake among medical students (21.2% vs. 8.2%). Notably, 41.3% of non-medical students and 7.5% of medical students had never heard of HPV vaccination. Willingness to vaccinate against a cancer-associated virus varied according to perceived infection risk. Conclusions: These findings highlight the need for targeted educational interventions to improve awareness of HPV–cancer links and risk perception, as well as to ensure ongoing education of both HPV-vaccinated and unvaccinated individuals to support informed health decisions and vaccine acceptance. Full article

In multi-turn conversational AI, individually innocuous personally identifiable information (PII) fragments disclosed across successive turns can accumulate into a re-identification risk that no single utterance reveals on its own. Existing PII detectors operate on isolated utterances and therefore cannot track this cross-turn evidence build-up. We propose a stateful middleware guardrail whose core design principle is speaker-attributed entity isolation: every extracted PII fragment is attributed to its originating conversational participant, and evidence is accumulated in entity-isolated subgraphs that prevent cross-entity contamination. The system signals re-identification onset $t_{\mathrm{pred}}$ at the earliest turn where combination-based rules grounded in the uniqueness literature are satisfied. On a 184-record template-synthetic evaluation corpus, the gated NER configuration leads on primary timeliness (OW@5 $= 73.4\%$, MAE$= 1.357$ turns); the full system achieves OW@5 $= 70.7\%$ with MAE $= 2.442$ turns as an alternative operating mode for ambiguity-sensitive disclosure patterns. We further evaluate behavior on a 300-record mutation stress set, test RULE_B on the ABCD external corpus, and supplement RULE_A evaluation with both a proxy-labeled transfer analysis on PersonaChat and a manual annotation study on 151 Switchboard dialogues. The reported results should be interpreted as an initial empirical reference point rather than a sufficient endpoint for autonomous runtime enforcement. Full article

The advancement of generative artificial intelligence (GAI) in healthcare is driving a transition from dialogue-based medical chatbots to workflow-embedded clinical AI agents. These agentic systems incorporate persistent state management, coordinated tool invocation, and bounded autonomy, enabling multi-step reasoning within institutional processes. As a result, traditional response-level evaluation frameworks are insufficient for understanding system behavior. This review provides a conceptual synthesis of the evolution from conversational systems to agentic architectures and proposes a system-level modeling framework for ethical clinical AI agents. We identify core architectural dimensions, including autonomy gradients, state persistence, tool orchestration, workflow coupling, and human–AI co-agency, and examine how these features reshape bias propagation pathways, error cascade dynamics, trust calibration, and accountability structures. Emphasizing that ethical risks emerge from longitudinal system interactions rather than isolated outputs, we argue for embedding fairness constraints, transparency mechanisms, and lifecycle governance directly within AI design. By outlining trajectory-level evaluation strategies, equity-aware development approaches, collaborative oversight models, and adaptive regulatory frameworks, this paper establishes a foundation for the responsible and trustworthy integration of agentic AI in healthcare. Full article

Background/Objectives: Blunt abdominal trauma is a frequent challenge in emergency medicine, but its diagnosis and treatment become significantly more complex when rare anatomical anomalies are present. Atypical anatomy may mask symptoms or mimic other acute abdominal conditions, causing delays in treatment. The aim of this paper is to review the literature on six rare anatomical anomalies and their impact on the consequences of blunt abdominal trauma. Methods: A Narrative literature review was undertaken, covering PubMed, Scopus, Web of Science and Google Scholar databases, analysing publications from 1960 to 2025. Case reports and case series (91 patients in total) with confirmed organ damage following blunt trauma in the course of: duodenal diverticulum, Meckel’s diverticulum, splenic torsion, rupture or torsion of the accessory spleen, visceral inversion (situs inversus) and horseshoe kidney. Results: Demographic analysis revealed a predominance of perforations of the duodenal diverticulum in older women (mean age 62 years), while younger men predominated in all other groups. The clinical picture was often non-specific or misleading, especially in situs inversus, where the location of pain did not correlate with the typical topography of organs. Contrast-enhanced computed tomography (CECT) has proved to be a key diagnostic tool, surpassing ultrasound/FAST scans due to its ability to provide precise anatomical imaging. Surgical treatment was predominant (100% in Meckel’s diverticulum, 95% in duodenal diverticulum), while conservative treatment was effective in horseshoe kidney injuries (94.8%). Mortality was highest in situs inversus (29%) and duodenal diverticulum perforation (20%). The vast majority of these fatal cases occurred in the era of modern computed tomography, suggesting that the therapeutic challenges stem directly from the specific nature of these anomalies, rather than from past diagnostic limitations. Conclusions: Anatomical anomalies significantly modulate the clinical manifestations of blunt abdominal trauma, increasing the risk of diagnostic errors. Early contrast-enhanced computed tomography and awareness of these rare pathologies are crucial for appropriate management and improved prognosis. Full article

Mediterranean forests are increasingly exposed to climate-related risks, including large wildfires, prolonged droughts and rural abandonment, making sustainable forest management (SFM) a key element for climate adaptation and territorial resilience. However, despite its recognised importance, the social acceptance of SFM remains insufficiently understood, particularly in relation to how public perceptions are shaped by media narratives and information ecosystems. This study addresses this gap by analysing the relationship between media framing and social acceptance of SFM in a Mediterranean context. A mixed-methods approach was applied in the Valencian region (Spain), combining (i) a systematic analysis of conventional and digital media, (ii) a system mapping exercise to identify dominant narratives and communication dynamics, and (iii) a population survey (n = 1070) focused on perceptions of forests, climate change and forest management. The results reveal a high level of environmental concern and climate awareness, coexisting with limited knowledge of SFM and simplified or distorted perceptions of forest dynamics. Media coverage is predominantly reactive and event-driven, strongly focused on wildfire events, while preventive and adaptive forest management practices remain largely invisible. In this context, support for SFM increases significantly when management practices are clearly explained and contextualised, indicating that resistance is more closely related to communication gaps than to ideological opposition. These findings highlight the critical role of media framing and communication processes in shaping the social acceptance of SFM. The study contributes to the literature by integrating media analysis and social perception within a forest governance perspective, and provides empirical insights to support more effective communication strategies and policy design in Mediterranean regions facing increasing climate pressures. Full article

Smart-meter theft detection requires learning from fine-grained electricity consumption data, whose centralized processing poses significant privacy risks. Federated learning (FL) mitigates these risks by decentralizing training, but providing rigorous user-level differential privacy (DP) under non-IID data and heterogeneous client behavior remains challenging. Existing DP-FL approaches rely on fixed global clipping bounds for client updates, which substantially overestimate sensitivity when privacy loss is composed using Rényi Differential Privacy (RDP), zero-Concentrated DP (zCDP), or Moments Accountant (MA) frameworks, leading to excessive noise and degraded utility. This work proposes an adaptive clipping-based RDP accountant that incorporates empirical, round-wise update magnitudes into privacy accounting by rescaling each round’s RDP contribution according to the observed clipping ratio. The method is optimizer-agnostic and is evaluated with FedAvg, FedProx, and SCAFFOLD on the SGCC smart-meter theft dataset under IID and Dirichlet non-IID partitions. Experimental results show consistently tighter privacy bounds and improved model utility compared to classical DP accountants, demonstrating the effectiveness of sensitivity-aware privacy accounting for practical differentially private FL. Full article