Search Results (5,291)

Aquatic environments that support tourism, including coasts, lakes, reservoirs, and estuaries, are experiencing accelerating eutrophication worldwide. This trend increases the frequency and intensity of algal blooms. These blooms undermine ecosystem services and weaken the socio-economic performance of destination areas. Despite these challenges, existing research remains fragmented. Aquatic sciences mainly examine nutrient enrichment and bloom dynamics. In contrast, tourism studies often treat blooms as episodic disturbances and rarely integrate exposure pathways, risk communication, or feedback to destination governance. This review synthesizes evidence across freshwater and marine systems to develop a coupled tourism–water ecosystem perspective. We link eutrophication drivers and bloom typologies to three dimensions. These are the degradation of tourism-supporting ecosystem services, compound health stressors, and communication filters. The first includes losses of water clarity and aesthetic value. The second involves multi-route exposure through contact, inhalation, and seafood ingestion. The third shapes perceived safety, trust, and behavioral adaptation. We further connect perceived health risks to observable tourist behaviors, including cancellation, destination substitution, and activity avoidance. These micro-level responses can aggregate into market-level demand contractions and consumption reallocation. They can also trigger regional economic cascades, including public management costs, employment impacts, and long-term reputational damage. Crucially, tourism is not merely a victim of blooms. It can also act as a reinforcing anthropogenic driver through wastewater burdens, infrastructure expansion, and pulse pressures. These pressures lower ecological resilience, especially under warming and hydrological stabilization. Finally, we identify governance leverage points. These include early-warning systems, threshold-based graded interventions, transparent risk communication, and integrated social–ecological modeling. These strategies can reduce uncertainty-driven losses and support adaptive destination management. Overall, this review reframes algal blooms as systemic social–ecological risks. It provides a structured basis for future empirical attribution and policy design in tourism-dependent waters under climate stress. Full article

The economic resilience of agricultural enterprises is increasingly relevant for maintaining processing continuity and food quality in highly perishable agro-food chains. This study examines the associations between financial knowledge, financial management competency, business liquidity, and operational food-processing continuity in Türkiye’s tea sector. A quantitative cross-sectional design was employed, using structured survey data from 203 senior managers across 86 public and private tea-processing firms in Rize Province. The data were analysed using Ordinary Least Squares regression, mediation analysis, exploratory factor analysis, and robustness checks in accordance with OECD/INFE guidelines. Results indicate a significant deficit in theoretical financial knowledge (mean score: 4.47/10) alongside widespread overconfidence among 85% of managers. Applied financial management competency is positively associated with perceived business liquidity (β = 0.336, p < 0.001), suggesting that practical budgeting, cash-flow planning, and financial decision-making capabilities are relevant to maintaining operational funding capacity. In contrast, cash-flow difficulties are not significantly explained by firm-level financial knowledge, managerial competency, liquidity, or ownership structure (R² = 0.014, p = 0.722), indicating that these difficulties may reflect broader seasonal and sector-wide financing constraints. The findings challenge the assumption of a linear relationship between theoretical financial knowledge and managerial outcomes. They suggest a dual policy approach that combines applied financial management training with structural financing mechanisms to ensure the continuity of fresh leaf procurement and processing. While the study does not directly measure food safety, post-harvest losses, or SDG outcomes, the results have potential implications for reducing processing disruptions and supporting more resilient agro-food processing systems. Full article

To solve the low-precision prediction problem of noisy non-stationary goaf subsidence sequences, this study aims to establish a high-accuracy hybrid prediction model for mining surface deformation monitoring. The Global Navigation Satellite System (GNSS) monitoring data of surface subsidence in goaf areas exhibits non-stationary and noisy characteristics, which limits the accuracy of traditional prediction models. In this paper, a hybrid prediction model, namely the Improved Radial Movement Optimization–Variational Mode Decomposition–Gated Recurrent Unit (IRMO-VMD-GRU) model, is proposed. The IRMO algorithm is employed to globally optimize the key parameters of VMD, achieving adaptive and stable decomposition of the settlement sequences. The obtained Intrinsic Mode Function (IMF) sub-sequences are input into the GRU network for independent training and prediction, followed by superposition and reconstruction. The model is validated using the GNSS monitoring data from three monitoring points at a coal mine in Shaanxi Province, China. The results show that the proposed model outperforms the comparison models in all four evaluation indicators, namely Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R²), with all R² values exceeding 0.8. The model demonstrates superior fitting performance, correlation, and generalization ability, which provides important practical technical support for goaf subsidence early warning, geological disaster prevention and engineering safety management in mining areas. Full article

Real-time telemetry is essential for performance optimization and safety in motorcycle racing, yet commercial solutions remain proprietary, expensive, and poorly extensible. This paper presents the design, implementation, and experimental evaluation of an open-architecture embedded telemetry unit built around the STM32H745 dual-core microcontroller. The system integrates a u-blox ZED-F9P RTK-GNSS receiver, a Bosch BNO085 9-DoF IMU with on-chip sensor fusion, a CAN-FD interface for powertrain data acquisition, and a SIM7600E-H 4G/LTE module for real-time remote streaming, all housed in a 3D-printed vibration-resistant enclosure. The firmware employs deterministic dual-core task partitioning: the Cortex-M7 core handles sensor fusion and CAN-FD at high frequency, while the Cortex-M4 core manages 4G communication and microSD logging. We explicitly delimit the scope of the evidence presented: CAN-FD powertrain acquisition and end-to-end operational reliability are experimentally validated on real circuit data spanning four campaigns, over 100 laps, and 5.8 h of logging—with sustained acquisition of 13 powertrain channels at speeds up to 185 km/h and zero system resets or data-integrity errors. In contrast, RTK positioning accuracy (2.5 cm CEP), sensor-fusion latency (sub-2 ms at the 99th percentile), 4G-uplink reliability, and thermal margins are characterized through manufacturer specifications, Monte Carlo simulation, and analytical models, with a fully instrumented end-to-end measurement campaign identified as the immediate next step. The 50 Hz effective positioning rate combines 25 Hz GNSS with IMU interpolation. With a bill of materials of approximately EUR 265, the platform offers an order-of-magnitude cost reduction over commercial alternatives while providing full openness and extensibility for distributed intelligence applications. Full article

Driven by global population aging, long-term in-home and institutional elderly care faces challenges in delivering continuous, privacy-aware, and resource-efficient safety and health monitoring. Existing edge-based solutions struggle to jointly balance detection accuracy, privacy, and resource overhead during continuous operation, and often have limited situational awareness and inflexible management. We propose EdgeElderCare, a resource-aware, scene-adaptive edge-cloud collaborative system for continuous elderly safety and health monitoring. Its contributions are threefold: (1) a scene-adaptive multi-sensor task-sharing architecture that deploys vision-based fall detection in public areas and privacy-aware millimeter-wave radar in private spaces. Combined with edge-side task scheduling, it provides spatially complementary coverage of public and private areas, mitigates the accuracy–privacy conflict, and reduces computing and bandwidth consumption relative to data-level fusion; (2) a lightweight myocardial infarction detection module deployed on an edge platform, enabling local ECG analysis with low resource overhead; (3) a 3D digital-twin edge-cloud management platform that maps multi-source sensing data to a virtual scene in real time and supports hierarchical visual alerting. Experiments in a real nursing home environment show that the system operated stably on resource-constrained edge hardware: UWB positioning achieved centimeter-level RMSE, visual fall detection reached a recall of 0.90, millimeter-wave radar fall detection achieved accuracy, and F1 above 0.90, and myocardial infarction detection exceeded 0.99 accuracy on the public PTB/PTB-XL benchmark. These results indicate an engineering-feasible approach to intelligent elderly care. Larger-scale and longer-term validation remains the focus of future work. Full article

The transition to automated vehicles (AVs) introduces complex human factors and system-level challenges across Society of Automotive Engineers (SAE) Levels 0–5, with profound implications for the long-term viability of future transport infrastructure. Drawing on a synthesis of socio-technical, cognitive, and behavioural adaptation theories, this study develops an integrated framework to analyse the evolving relationships among driving automation, human behaviour, system risks, and urban sustainability. The findings demonstrate that human-factor risks are inherently nonlinear, meaning they do not decrease proportionally as technology advances; instead, risk profiles peak significantly at intermediate automation levels (SAE 2–3) due to supervisory fatigue and delayed takeovers, introducing severe traffic flow volatility and localised micro-congestion that directly compromise the environmental efficiency of sustainable transport systems. As these risks reconfigure into institutional and digital infrastructure dependencies at higher levels (SAE 4–5), the primary constraint shifts toward network readiness. Through an analysis of real-world AV deployment case studies and a structured narrative literature review, this paper identifies critical operational discontinuities and mixed-traffic complexities that threaten urban grid resilience. This study proposes a conceptual framework that translates these cross-level socio-technical insights into actionable deployment pathways, providing policymakers with adaptive governance models, transportation planners with mixed-traffic management strategies aimed at preserving network efficiency, infrastructure agencies with physical and digital readiness criteria for long-term asset sustainability, and AV developers with human–machine interface optimisation frameworks to secure human-centric safety within sustainable smart city networks. Full article

Water resources safety is a crucial prerequisite for nuclear power development and a key component of the safety system for inland nuclear power. Based on an analysis of the influencing factors of water resources safety during the site selection stage of inland nuclear power, this paper constructs an evaluation index system for water resources safety in this stage using the Pressure–State–Response (PSR) model. Combining current technical standards related to nuclear power site selection with Strictest Water Resources Management System formulated by the Chinese government, the evaluation standards for water resources safety during the site selection stage of inland nuclear power are established. Two water resources safety evaluation models for inland nuclear power plant site selection are presented, employing the Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation Method (FCEM) respectively. Finally, the water resources safety evaluation system established in this paper is applied to the water resources safety evaluation during the site selection stage of Xiaomoshan Nuclear Power Station. The evaluation results of the two models are basically consistent, and both conclude that the water resources safety during the site selection stage of Xiaomoshan Nuclear Power Station could be basically guaranteed. This provides an effective means for the water resources safety evaluation during the site selection stage of inland nuclear power plants. Full article

Accurate Unmanned Aerial Vehicle (UAV) position estimation is the cornerstone of urban low-altitude safety management systems. Time Difference of Arrival (TDOA) and Remote Identification (Remote ID) are widely used surveillance technologies with complementary characteristics. TDOA provides high-rate updates but suffers from geometry-induced horizontal–vertical anisotropy and multipath effects, while Remote ID supplies absolute state information yet struggles with intermittent sampling and packet loss. Existing fusion schemes typically address these issues in isolation: sequential filtering manages asynchrony but assumes Gaussian noise, robust estimators suppress outliers at the cost of discarding valid data, and coupled-filter architectures allow vertical anomalies to contaminate horizontal estimates through the Kalman gain cross-coupling. No prior framework jointly handles structural TDOA altitude jumps, stochastic Remote ID timing jitter, and the geometric anisotropy between estimation subspaces within a single coherent pipeline. To bridge this gap, we propose a Hybrid Conditional Kalman Filter (HCKF) framework comprising three integrated modules. First, a kinematics-based temporal alignment module maps asynchronous measurements onto a uniform timeline and predicts missing samples, resolving cross-modal time mismatches. Second, a measurement quality evaluation mechanism detects TDOA altitude steps via robust two-layer stratification and scores Remote ID timing irregularity through a confidence mapping, converting these anomalies into dynamic covariance adjustments and weight caps without discarding observations. Third, a Subspace-Decoupled Fusion strategy exploits the physical insight that TDOA horizontal precision derives from hyperbolic intersection geometry, whereas its vertical estimates suffer from weak observability due to near-coplanar ground-station deployment . By applying entropy-guided weighting in the horizontal plane and a conditional Remote ID-dominant rule in the vertical axis, this design prevents cross-dimensional error propagation. The framework was validated using three real-world flight missions at distinct altitudes (255 m, 345 m, and 440 m) totaling 13.51 km of flight distance, with RTK serving as ground truth. HCKF reduces the Root Mean Square Error by over 40% relative to single-source baselines (95% bootstrap confidence interval: [35.2%, 48.7%]), and paired Wilcoxon signed-rank tests confirm statistically significant improvement ($p<0.01$) over standard EKF, Covariance Intersection, and Iterative CI across all three tracks. Full article

Vehicle-to-Everything (V2X) communication is a critical enabler of autonomous driving, supporting real-time information exchange among vehicles, roadside infrastructure, pedestrians, and cloud services. However, the security of current V2X systems largely relies on classical cryptographic mechanisms, which are expected to become vulnerable in the presence of large-scale quantum computers. Given the long operational lifespan and stringent safety requirements of autonomous vehicular networks, the transition toward quantum-resistant authentication and key management mechanisms has become increasingly important. This paper presents a comprehensive survey of post-quantum Authentication and Key Agreement (AKA) protocols for secure V2X communications. The survey systematically reviews V2X communication architectures, security and privacy requirements, existing authentication frameworks, and emerging post-quantum cryptographic approaches. Representative AKA schemes and NIST-standardized post-quantum algorithms are comparatively analyzed in terms of security strength, computational complexity, communication overhead, storage requirements, scalability, and deployment suitability for resource-constrained vehicular environments. The survey further examines practical implementation challenges, including latency constraints, bandwidth limitations, signature size expansion, memory consumption, and hardware resource requirements. The analysis reveals that achieving quantum-resistant security in V2X networks requires balancing strong cryptographic protection with the stringent performance demands of safety-critical vehicular applications. While recent post-quantum approaches offer promising security guarantees against quantum adversaries, their practical deployment remains constrained by computational and communication overhead. Finally, this survey identifies key research gaps and outlines future directions for the development of lightweight, scalable, and quantum-resilient AKA frameworks capable of supporting next-generation autonomous transportation systems. The findings provide researchers and practitioners with a structured understanding of the opportunities, limitations, and challenges associated with securing future V2X communications in the quantum era. Full article

Nonlinear Model Predictive Control (NMPC) has garnered significant attention in autonomous systems due to its ability to predict future states and manage complex vehicle dynamics. However, the adaptability of existing NMPC methods is constrained by having to manually set the weight coefficients in the NMPC cost function. This study aims to explore a novel approach that integrates NMPC with Reinforcement Learning (RL), specifically employing Proximal Policy Optimization (PPO), to dynamically adjust NMPC weight matrices. The investigation begins by establishing a physics-based model for a two wheeled differential drive vehicle. A PPO model is then trained and deployed in real time to adapt to the NMPC weight matrices, achieving a 71% reduction in tracking error compared with the NMPC baseline. Importantly, the performance gain arises from PPO’s ability to reshape the NMPC cost function in real time, amplifying both orientation and lateral penalties in curves while relaxing them on straights, thereby enabling adaptive trade-offs between accuracy and control effort that static-weight NMPC cannot achieve. To enhance safety, the controller is integrated with a Control Barrier Function (CBF) layer for real-time obstacle avoidance, while PPO’s real-time weight adaptation contributes to improved tracking performance relative to NMPC+CBF. Finally, robustness evaluations under friction uncertainty, sensor noise, and path disturbances demonstrate that the PPO+NMPC+CBF method maintains reliable tracking accuracy and safety margins. Full article

Endoscopic resection (ER) has become the preferred curative-intent treatment for early upper gastrointestinal cancer, given its superior safety profile compared to surgery. Over the past decade, technological and procedural innovation has substantially expanded the scope, safety, and precision of endoscopic submucosal dissection (ESD) and related techniques. This review synthesises current evidence on key advances relevant to upper gastrointestinal ESD practice. Enhanced imaging modalities have improved lesion detection and characterisation, as well as recognition of intraoperative anatomical structures during third-space endoscopy. A new generation of therapeutic endoscopes combines high-definition optics with substantially improved tip-down angulation and channel size, addressing a longstanding gap between diagnostic-class image quality and procedural capability. Resection strategies—including mechanical traction systems, saline immersion therapeutic endoscopy (SITE), and luminal drainage techniques—have reduced procedural complexity and improved dissection conditions. Dedicated closure technologies have improved management of large resection defects, potentially reducing resection-related morbidity. Deep resection techniques, including submucosal tunnelling endoscopic resection (STER), device-assisted endoscopic full-thickness resection (FTRD), knife-assisted full-thickness resection (kFTR), and endoscopic intermuscular dissection (EID), are extending organ-preserving resection to deeply invasive cancers and subepithelial lesions. Management of non-curative ESD resections is being refined through multicentre risk stratification studies. Advances in simulation, competency-based training, and artificial intelligence hold promise for standardising technique acquisition and real-time procedural support. Together, these innovations are reshaping upper gastrointestinal oncology by positioning minimally invasive, organ-preserving digestive endoscopy as a central therapeutic strategy. Full article

The safe disposal of sharps, particularly acupuncture and dry needling needles, remains a challenge in clinical and therapeutic environments, where inadequate management increases the risk of occupational injuries and infections. Commercial needle disposal devices are often costly, non-portable, and closed-source, limiting their adoption in small clinics and low-resource contexts. This work presents the design, construction, and validation of an open-source electric needle incinerator developed as a low-cost, safe, and reproducible alternative for biomedical waste management. The device was designed using accessible materials, 3D-printed components, and standard electronic parts, ensuring ease of replication. Detailed build and operating instructions are provided, to facilitate reproduction and future development of the system. Validation tests confirmed that the prototype incinerates individual needles in 3–5 s, processing typical sessions of 5–20 needles without performance degradation. Safety was ensured through thermal insulation, protective casing, and compliance with international standards. The fabrication cost of approximately 199 USD represents a reduction of over 65% compared to commercial devices priced at 600–1500 USD. By openly releasing the design, this contribution supports the hardware community with a replicable solution that enhances occupational safety, reduces costs, and fosters innovation in therapeutic and educational contexts. Full article

Artificial intelligence (AI) is rapidly transforming healthcare through applications in clinical decision support, diagnostic imaging, population health management, and workflow optimization. Despite these advances, real-world deployment continues to expose critical challenges related to safety, bias, transparency, and integration into clinical workflows. Algorithmic bias can exacerbate health disparities, limited explainability may undermine clinician trust, and insufficient validation and post-deployment monitoring can compromise patient safety. Although the World Health Organization (WHO) has established six ethical principles for AI in health, including autonomy, well-being and safety, transparency, accountability, equity, and sustainability, translating these high-level principles into practical and enforceable governance mechanisms remains a persistent challenge. This narrative review synthesizes insights from bioethics, health policy, computer science, and clinical medicine to identify gaps in current AI governance approaches and proposes a lifecycle-aligned governance-by-design framework that operationalizes WHO ethical principles across key stages of the healthcare AI lifecycle, including data collection, model development, validation, deployment, and post-deployment monitoring. The framework integrates concrete governance mechanisms such as consent governance, fairness evaluation, external validation, explainability, clinician oversight, and continuous performance monitoring. Overall, this work advances a practical, lifecycle-integrated approach to AI governance and provides a structured foundation for developing safe, equitable, and trustworthy AI systems in healthcare. Full article

Due to the environmental concerns associated with petroleum-based plastics, industry and academia have directed increasing attention toward marine-derived biodegradable biopolymers, particularly those obtained from seaweed. In line with global efforts to enhance resource efficiency and sustainability by introducing non-fossil raw materials into the circular economy, seaweed valorization has emerged as a promising pathway. Seaweeds are attractive feedstocks due to their biodegradability, non-toxicity, antioxidant activity, and excellent film-forming capacity. This review provides a critical and application-oriented overview of seaweed biomass for food packaging applications by comparatively discussing the relationship between seaweed composition, extraction technologies, material functionality, packaging performance, and regulatory considerations. Emphasis is placed on the role of structural biopolymers and bioactive compounds in the development of passive, active, and intelligent packaging systems. Recent advances in extraction technologies, polymer modification strategies, and incorporation of functional additives are critically discussed in relation to their influence on the physicochemical, mechanical, barrier, antioxidant, and antimicrobial properties of seaweed-based composites. Furthermore, the review highlights key challenges limiting industrial implementation, including high hydrophilicity, high variability between the batches, energy-intensive drying processes, regulatory compliance, migration safety, and long-term material stability. Overall, seaweed-derived materials demonstrate strong potential as sustainable alternatives to conventional packaging systems, particularly in food applications. However, further optimization of processing technologies, material standardization, techno-economic feasibility, and end-of-life management are still required before large-scale commercialization can be achieved. Full article

This study presents a comparative analysis of six DFS implementations representing different maturity levels and investigates the systemic gap between technological capabilities and regulatory approaches. A structured narrative review with case-based analysis was conducted using the Scopus database (2015–2026) with six targeted queries. The case selection followed the PICo protocol. An original ten-criterion DFS maturity assessment rubric—grounded in the Technology Readiness Level (TRL), Integration Readiness Level (IRL), and Digital Twin Maturity Model frameworks—was applied to all six cases. Inter-rater validation yielded substantial agreement (κw = 0.797; unweighted κ = 0.674 [95% CI: 0.509, 0.839]). The results indicate a clear maturity gradient (Dimension X: 4–9 points; Dimension Y: 2–8 points). Benefits reported in the analysed primary studies include up to a 55 s reduction in evacuation time, a 72% improvement compared with static signage, and a 34-percentage-point increase in evacuation success rate under simulation-based conditions. Five normative recommendations are proposed to address the structural regulatory gap between current prescriptive frameworks and DFS deployment in Poland and the EU. This study argues that prescriptive rules should remain the baseline, whereas complex facilities may adopt performance-based DFS solutions, provided that equivalence to conventional protection levels is rigorously demonstrated. From a sustainability perspective, the study frames DFS as a dynamic safety layer that supports occupant protection, operational resilience, and lifecycle adaptability in complex buildings exposed to uncertain fire and crowd conditions. Full article