Search Results (30,711)

Background: In the wake of the COVID-19 pandemic, the dietary landscapes of young adults have been profoundly reshaped. As social restrictions ease, the resurgence of dining out presents new behavioral shifts regarding health and safety. Objective: This study investigates the post-pandemic experiences of young adults in Hong Kong, focusing on the burgeoning phenomenon of eating out of home and its complex influence on eating habits and food hygiene consciousness. Methods: This qualitative study utilized a phenomenological approach to explore participants’ lived experiences. Semi-structured interviews were conducted with 12 young adults in Hong Kong to gather narratives regarding their dining practices. The data were analyzed using thematic analysis to identify recurring patterns associated with their return to public dining spaces. Results: Three core themes emerged: (i) confined palates: the remaking of the Hong Kong meal in the shadow of a pandemic; (ii) shared screen: mediating hunger from the home-as-hub; and (iii) watchful guard: the moralization of the meal amidst viral uncertainty. Conclusions: These findings dissect the critical, evolving relationship between contemporary consumption patterns and health maintenance. While the small size limits statistical generalizability, the study suggests that post-pandemic dining involves a modified reality of sustained hypervigilance. These insights offer a basis for developing sensitive and targeted public health strategies that resonate with the altered dietary realities of young adults in a post-pandemic world. Full article

Modern microgrids increasingly rely on learning-based energy management systems (EMSs) for real-time decision-making, yet remain vulnerable to cyber–physical disturbances, sensor tampering, and model uncertainty. Existing resilient control and robust reinforcement learning methods provide useful foundations, but rarely address adversarial measurement perturbations that distort belief evolution under partial observability. This gap is critical, as structured perturbations in sensing channels can destabilize learning-based policies and propagate into voltage-regulation violations. This paper proposes an adversarially robust reinforcement learning framework for energy management with voltage regulation under partial observability in microgrids. The EMS decision-making problem is formulated as a partially observable Markov decision process (POMDP) that accounts for adversarial measurement perturbations, belief evolution, and system-level economic and voltage constraints. To avoid excessive conservatism under worst-case uncertainty, an adversary-aware belief construction based on adversarial belief balancing (A3B) is employed to focus on policy-relevant perturbations. Building on this belief representation, an adversarially robust learning framework is developed by incorporating adversarial counterfactual error (ACoE) as a learning regularization mechanism, enabling a balance between nominal operating efficiency and robustness under adversarial measurement distortion. The case study is conducted on a medium-voltage radial distribution feeder (IEEE 123-Node Test Feeder). Case study results demonstrate that the proposed ACoE-regularized policies substantially reduce voltage-deficit events, improve policy stability, and maintain operational constraints under adversarial perturbations, consistently outperforming standard proximal policy optimization (PPO)-based controllers. These results indicate that counterfactual-aware, belief-based learning substantially enhances voltage quality and operational resilience in microgrids with high penetration of distributed energy resources. Full article

Background: Annuloplasty ring sizing is critical for durable outcomes in surgical mitral valve repair (MVr). However, there is no clear consensus on optimal sizing strategies. Artificial intelligence (AI)-based imaging tools may help to reduce uncertainty in preoperative decision-making by providing objective, reproducible and reliable measurements. This study evaluated the predictive capability of a fully automated, computed tomography (CT)-based AI-driven tool for annuloplasty ring sizing in patients undergoing minimally invasive MVr (MI-MVr). Methods: A total of 71 consecutive patients undergoing MI-MVr for Carpentier type II mitral valve insufficiency during the study period were included. Preoperative CT scans were analyzed using a cloud-based, fully automated AI tool to quantify mitral valve geometric parameters. Correlations between AI-derived measurements and implanted ring sizes were assessed using the Pearson correlation test. Univariable and multivariable linear regression analyses were performed to identify independent predictors of ring size selection. Results: Several AI-derived parameters correlated significantly with implanted ring size, with the strongest correlations observed for commissural width (R = 0.693, p < 0.001) and mitral annular area (R = 0.693, p < 0.001). In multivariable regression analysis, these parameters were the strongest predictors of annuloplasty ring size (R² = 0.504, p < 0.001). Using this model, accurate annuloplasty ring sizing could be predicted in 78.8% of patients. There were no in-hospital mortality and residual mitral regurgitation at discharge. Conclusions: A fully automated, CT-based AI-driven tool demonstrated good accuracy for preoperative annuloplasty ring size prediction in MI-MVr and may have the potential to support surgical decision-making, reduce operator dependence, and improve reproducibility. Full article

Background/Objectives: Suicide attempts represent a major global health problem. Traumatic suicide methods, such as falls from great heights, stab wounds, and gunshot wounds, frequently result in severe or fatal injuries. The COVID-19 pandemic, as well as broader societal stressors including economic uncertainty and geopolitical conflicts, has substantially increased psychological stress in the population and has been discussed as a potential influencing factor for suicidal behavior. The aim of this study was to analyze severe traumatic suicide attempts and to evaluate the potential influence of the COVID-19 pandemic in a multicenter analysis of the TraumaRegister (TR) DGU^®. Methods: This retrospective multicenter analysis is based on the TraumaRegister DGU^®, a standardized database for seriously injured patients. Patients from Germany, Austria, and Switzerland from 2010 to 2023 with an Injury Severity Score (ISS) ≥ 9, an age ≥ 10 years, and a documented suicide attempt, who arrived at the hospital alive, were included. Results: Among severely injured trauma patients recorded in the registry, 12,247 (4.4%) cases were classified as suspected traumatic suicide attempts. Severe traumatic suicide attempts showed a clear age-dependent distribution, with a marked increase from adolescence and a plateau between 20 and 55 years of age. Both the mean age of the general population and the age of patients with suicide attempts increased over the study period. This trend was reflected in the rise in the ≥70-year age group from 13.6% in 2010 to 19.6% in 2023. The most common method was jumping from a height greater than 3 m (65.3%), followed by stab wounds (11.9%) and gunshot wounds (8.0%). While a significant decline in severe traumatic suicide attempts was observed between 2010 and 2019, a significant increase to 4.5% occurred in 2020, remaining at a comparable level in the following years. Sex-specific differences were observed, with penetrating injuries occurring more frequently in men, whereas jumps from heights > 3 m were more common among women. The highest hospital mortality was observed in gunshot injuries (67.9%). Conclusions: This study demonstrates an increase in severe traumatic suicide attempts in 2020 that persisted at a similar level until 2023. Sex-specific differences in suicide methods highlight the need for targeted prevention strategies. In addition, demographic aging is reflected in the increasing proportion of suicide attempts among older individuals, emphasizing the need for age-specific prevention measures. The relatively high survival rate after certain methods, particularly after falls from height (77%), underlines the importance of structured postoperative psychiatric care pathways. These findings specifically reflect traumatic suicide attempts resulting in severe injury and requiring trauma center treatment. Full article

Accurate hour-ahead forecasting of solar photovoltaic (PV) power is essential for risk-aware decision-making in power systems with increasing renewables. Although recent studies emphasize complex deep learning architectures, it remains unclear whether such complexity provides tangible benefits at very short forecasting horizons, particularly when forecast uncertainty is considered. This study evaluates deterministic and probabilistic hour-ahead PV forecasting using models of varying complexity, including persistence, linear autoregressive models with exogenous inputs, ridge regression, DLinear, and a vanilla long short-term memory (LSTM) network. Probabilistic forecasts were constructed using a unified, model-agnostic, adaptive conformal inference framework incorporating a daily miscoverage reset tailored to the diurnal characteristics of PV generation. Deterministic results indicate that the LSTM achieves the lowest errors, with an RMSE of 0.336 kW (6.55% of rated capacity) and an MAE of 0.164 kW, compared to RMSE values of approximately 0.38–0.45 kW for linear models and persistence. Following conformal calibration, all models attain empirical prediction interval coverage close to the nominal 90% level (PICP ≈ 90.8–91.4%), with performance differences reflected in interval width and sharpness rather than coverage. Notably, linear models combined with adaptive calibration deliver probabilistic performance comparable to the LSTM at substantially lower computational cost. Full article

Urban Air Mobility (UAM) introduces new safety challenges as small unmanned aircrafts begin to operate at high density in complex urban environments. Traditional air traffic management (ATM) systems developed for manned aviation are unable to accommodate the autonomy, mission diversity, and dynamic obstacle conditions typical of low-altitude operations. This review examines recent research on mid-air collision risk and airspace safety modeling for UAM and identifies key challenges in adapting existing safety concepts to small-scale and autonomous flight. The study compares international management frameworks of the United States, Europe, and China. Then analyzes representative airspace structures such as Free, Layered, Zoned, and Pipeline configurations. It further reviews deterministic and probabilistic separation models, geometric and optimization-based avoidance strategies, and structured airspace approaches such as the virtual-tube concept for coordinated swarm navigation. The findings highlight the lack of integrated models that couple human, energy, and communication factors into quantitative risk assessment. The paper concludes by outlining future research needs in uncertainty modeling, digital-twin simulation, and interoperability to support safe and scalable UAM development. Full article

The purpose of this study is to develop and validate a System Dynamics (SD) model that illustrates how Artificial Intelligence (AI), including generative AI, alters project lifecycle behavior under a hybrid agile–predictive governance approach. The study method uses SD model to operationalize the PMBOK performance domains as an interconnected system of stocks, flows, and feedback loops. These constructs and their interaction represent delivery progress, stakeholder engagement, team capacity, measurement accuracy, governance alignment, and uncertainty exposure. Planning effectiveness is treated as an emergent performance indicator arising from the interaction of the planning-related feedback structures. The proposed model embeds AI levers for planning, risk, measurement, stakeholder sensing, and team support. A calibrated baseline model representing conventional project dynamics was validated in two ways. First it was validated structurally against PMBOK guidance and the SD literature. Secondly, it was validated behaviorally against stylized project trajectories. The AI-augmented variant was then simulated under identical initial conditions to assess marginal effects. Across multiple scenarios, AI integration reduced peak uncertainty exposure by up to 33%. Also, the AI-augmented system showed reduced planning effort by 15%, and improved monitoring and risk sensing by accelerating feedback and reducing delays by 25%. AI also improved measurement accuracy trajectories and accelerated cumulative delivery while lowering volatility in work completion rates. Governance coherence and development approach alignment improved, while stakeholder engagement and team capacity showed smaller changes. The results demonstrate that AI primarily acts as an enabler that strengthens high-impact feedback loops in planning, monitoring, and risk sensing within a hybrid methodology. AI also delineates boundaries where managerial judgment and cultural change remain critical for effective framework validation. Full article

Background: Despite major advances in antiretroviral therapy, HIV transmission remains an important public health challenge. Pre-exposure prophylaxis (PrEP) is a highly effective prevention strategy, offering a significant opportunity to further reduce new HIV infections through expanded access and optimized implementation. Methods: A national multidisciplinary consensus process was conducted to define principles and operational pathways for PrEP referral and linkage in Romania. Experts from different medical fields, professional societies, academic institutions, and community-based organizations participated in structured discussions. Results: The consensus highlighted relevant knowledge gaps related to HIV prevention and PrEP among specialists working outside the field of infectious diseases, including difficulty recognizing risk factors for HIV, uncertainty about next steps after identifying risk factors, and uneasiness discussing sexual health. The consensus also emphasized a shared commitment of these professional societies to address these gaps through pragmatic, specialty-adapted training. Key priorities included improved HIV risk recognition in routine care, development of communication skills, and clear referral pathways to PrEP services. Existing barriers were also discussed, underscoring the importance of multidisciplinary networks and community engagement. Conclusions: This consensus provides a structured, context-adapted framework to support the upcoming nationwide implementation of PrEP in Romania. By strengthening provider education, clarifying clinical pathways, and fostering interdisciplinary collaboration, it offers a foundation for equitable and sustainable HIV prevention. Full article

Geological hazard data exhibits high-volume and multi-type characteristics, specifically characterized by inherent complexity; measurement uncertainty; cross-source heterogeneity; underdeveloped semantic organization; and fragile inter-entity associations. Consequently, advanced modeling techniques coupled with robust extraction frameworks become imperative for effective unstructured data governance. To address this challenge, we propose a content–knowledge representation framework that decomposes and reconstructs disaster data using fine-grained content entities as base units. This approach allows for a unified description, objectification, ordering, hierarchical storage, and indexed categorization of unstructured information. Furthermore, we develop specialized text extraction algorithms tailored to document imagery and vector maps—facilitating the systematic application of information retrieval techniques while efficiently targeting specific thematic content. Our method outperforms two representative deep learning architectures (Fast CNN and FCN), demonstrating superior performance in segmenting target regions and precisely detecting textual elements, tables, and geographic features within complex datasets. By studying the modeling and extraction technology of unstructured geologic data, this paper establishes the value chain of geologic result data, which can provide strong support for digital management of geologic disaster data and improve work efficiency. Full article

The growth of human activity in cities is a key factor in the degradation of air quality. Numerous studies have demonstrated the link between air quality and the existence of dangerous and chronic diseases that are extremely costly for individuals and society. This study presents an analytical framework that compares fixed and mobile air-quality monitoring approaches in cities with limited resources, using Mohammedia city, Morocco, as an example. The framework centers on mobile monitoring units mounted on vehicles and equipped with affordable sensors, GPS technology, and wireless communication systems to track important pollutants, including fine particulate matter (PM_2.5 and PM₁₀) and harmful gaseous compounds (NO₂, SO₂, CO, O₃). The evaluation relies on scenario-based modeling, performance data from existing literature, and calculations of costs throughout the system’s lifetime. To enhance measurement reliability, the researchers developed a correction system that addresses measurement errors caused by temperature, humidity, vehicle speed, vibrations, traffic-related interference, operational interruptions, and communication limitations. The findings indicate that fixed monitoring stations deliver superior measurement precision, with estimated uncertainty ranging from ±1.2–2.5%, though their coverage area is restricted to 0.534 km² (representing 1.6% of Mohammedia). In comparison, the suggested mobile setup could potentially monitor 9.8 km², covering approximately 30% of the city, while decreasing infrastructure needs and setup time (2–4 h compared to 2–4 weeks). Over 10 years, the total cost is EUR 252,000 for mobile monitoring, compared with EUR 3.6 million for a network of 20 fixed stations. These results demonstrate that corrected mobile monitoring systems offer significant promise as an economical and sustainable approach for managing urban environmental conditions. Full article

Stephania tetrandra is a medicinal plant with ecological, germplasm, and economic value whose wild resources are increasingly constrained by overexploitation and climate change. To support conservation planning and sustainable cultivation, we quantified current and future potential habitat suitability across China using an ensemble species distribution modeling (SDM) framework and translated the outputs into climate-based priority areas for protection, germplasm safeguarding, monitoring, and phased cultivation trials. Occurrence records were compiled from multiple sources and preprocessed via cleaning and spatial thinning to reduce sampling bias. Current predictors were derived from WorldClim (1970–2000) and complemented with topographic and edaphic variables; future climates were represented by CMIP6 projections for the 2050s, 2070s, and 2090s under SSP1-2.6, SSP2-4.5, and SSP5-8.5. Multiple algorithms were trained in a consistent cross-validation workflow and filtered using AUC (ROC) and TSS before generating a weighted ensemble (EMwmean). Current projections indicate a well-defined suitability core in the humid subtropical monsoon region south of the Yangtze River. Nationally, high-, moderate-, and low-suitability areas were estimated at 51.90 × 10⁴ km², 22.95 × 10⁴ km², and 31.05 × 10⁴ km², respectively. Future impacts are dominated by suitability-grade reallocation rather than a collapse of total suitable extent. Under SSP5-8.5 in the 2090s, high suitability declines to 13.32 × 10⁴ km² (≈74% reduction), accompanied by contraction of stable habitat (48.95 × 10⁴ km²) and expansion of loss areas (33.64 × 10⁴ km²), while gains remain limited (4.30 × 10⁴ km²). Extrapolation diagnostics (Multivariate Environmental Similarity Surface, MESS; Most Dissimilar Variable, MoD) highlight elevated uncertainty in northwestern arid/high-elevation and strongly seasonal transition zones. Environmental-space niche overlap decreases moderately (Schoener’s D = 0.51–0.67), indicating niche displacement and a narrowing suitability window. These results represent potential climatic habitat suitability rather than guaranteed future occupancy. They support prioritizing in situ protection and germplasm safeguarding in areas that are currently highly suitable and remain comparatively stable under future climates, while treating marginal gain zones as candidates for monitoring and carefully phased cultivation or introduction trials. Full article

Green ammonia is increasingly recognised as a sustainability enabler for decarbonising fertiliser production, energy storage, and maritime transport, but offshore wind-to-ammonia pathways remain subject to significant economic and operational uncertainty. This study evaluated the techno-economic and sustainability performance of integrating power-to-ammonia (PtA) with an operating offshore wind farm in Denmark under three supply-chain scenarios (SCs): SC1, a fully offshore PtA with vessel-based ammonia transport; SC2, a fully offshore PtA with pipeline export; and SC3, a hybrid offshore–onshore configuration. An hourly dispatch framework allocated wind electricity between grid export and ammonia production by comparing incremental operating margins, while accounting for minimum-load, ramping, storage, and logistics constraints. Hourly wind generation and DK1 electricity-price data for 2020–2025 are used to construct a deterministic base case and a 30-year block-bootstrap Monte Carlo analysis. Sensitivity analysis is performed by varying electrolyser rated power over 10–200 MW and ammonia selling price over 1400–3200 €/tNH3, with additional breakeven-price estimation and flexibility cases based on reduced minimum-load requirements and faster ramping. A screening-level climate indicator was additionally reported by estimating potential CO₂ emissions avoided if delivered green ammonia displaces conventional natural-gas-based ammonia. Results indicated that SC3 is the most favourable configuration under the adopted assumptions, while overall project viability remained highly sensitive to PtA sizing, ammonia market value, operational flexibility, and the assumed infrastructure cost structure. Full article

Trajectory optimization of hypersonic vehicles face challenges from complex aerodynamic environments and multiple constraints, where traditional offline optimization methods struggle to meet real-time requirements. This study proposes a novel online trajectory optimization framework for hypersonic vehicles that integrates a multi-strategy improved whale optimization algorithm (MWOA) with an attention-mechanism Long Short-Term Memory (AM-LSTM) network. First, an offline trajectory dataset under aerodynamic uncertainties is generated using sequential second-order cone programming (SOCP). Subsequently, a multi-head attention mechanism is incorporated into the LSTM network to effectively capture sequential dependencies within the trajectory data. To automate the hyperparameter tuning of the AM-LSTM architecture, a multi-strategy improved whale optimization algorithm is developed, which incorporates circle chaotic mapping for population initialization, a nonlinear convergence factor to balance global and local search, and a dynamic golden-sine mutation strategy to enhance optimization robustness. The trained MWOA-AM-LSTM hybrid model is then employed for real-time trajectory generation. Numerical simulation results demonstrate that the proposed framework achieves superior terminal accuracy under aerodynamic perturbations, validating its effectiveness and robustness for hypersonic vehicle reentry trajectory optimization. Full article

Accurate pedestrian trajectory prediction and intention estimation are crucial for autonomous systems and intelligent transportation applications. However, existing methods often address these two highly correlated tasks in isolation and rely on static or heuristic interaction modeling, leading to insufficient adaptability and limited generalization capability in dynamic traffic scenarios. To this end, this paper proposes MTG-TPNet, a Multi-task dynamic Graph Transformer network for joint Trajectory Prediction and intention estimation. The research framework integrates three key innovations: First, a dynamic graph neural network enhanced with motion features, whose graph topology can be adaptively learned end-to-end based on semantic and motion contexts to accurately capture evolving interactions. Second, a multi-granularity attention mechanism that collaboratively fuses geometric proximity, semantic similarity, and physical hard constraints to achieve fine-grained modeling of spatiotemporal dependencies. Third, a dynamic correlation loss based on Bayesian uncertainty, which balances multi-task learning in an adaptive manner and encourages beneficial interactions across tasks. Extensive experiments on the publicly available PIE and ETH/UCY datasets demonstrate that MTG-TPNet achieves state-of-the-art performance. On the PIE dataset, the proposed model significantly outperforms the best baseline model in trajectory prediction metrics, achieving an Average Displacement Error (ADE) of 0.21 and a Final Displacement Error (FDE) of 0.29. This represents a 27.6% reduction in ADE while maintaining stability in intention estimation. Systematic ablation studies validate the effectiveness of each proposed module, with the model retaining an average performance of 69.3%. Furthermore, cross-dataset evaluations confirm its superior generalization capability. This study provides a powerful unified framework for robust pedestrian behavior understanding in complex urban traffic scenarios. Full article

We present PIRATE (Precision Imaging Real-time Autonomous Tracker and Explorer), a fully autonomous unmanned surface vehicle designed to enable self-operating data collection and persistent tracking of mobile underwater targets through the tight integration of acoustic localization, onboard visual perception, and closed-loop navigation. PIRATE employs a single mobile acoustic receiver to estimate target position using time-difference-of-arrival (TDoA) measurements acquired at different times and locations through planned autonomous motion and uses these estimates to drive adaptive vehicle behavior and activate fine-grained visual sensing in real time. This architecture enables sustained target-driven operation, in which navigation, acoustic monitoring, and visual processing are dynamically coordinated based on mission context and localization uncertainty. The system integrates real-time AI-based visual detection and tracking with automatic mission control, allowing visual perception to operate opportunistically within an acoustically guided tracking loop rather than as a standalone sensing modality. Field experiments in a shallow-water environment demonstrate reliable autonomous navigation, single-receiver acoustic localization with meter-scale accuracy, and stable onboard visual inference under sustained operation. By enabling coupled acoustic tracking and onboard visual perception in a fully autonomous surface platform free of external infrastructure, PIRATE provides a practical foundation for fine-scale behavioral observation, adaptive marine monitoring, and long-duration studies of mobile underwater organisms. We demonstrate this advantage with two possible applications. Full article