Search Results (10,744)

Ensuring data integrity and adaptability in aircraft health monitoring (AHM) is vital for safety-critical aviation systems. Traditional digital twin (DT) and federated learning (FL) frameworks, while effective in enabling distributed, privacy-preserving fault detection, lack mechanisms to remove the influence of corrupted or adversarial data once these have been integrated into global models. This paper proposes a novel FL–DT–FU framework that combines digital twin-based subsystem modeling, federated learning for collaborative training, and federated unlearning (FU) to support the post hoc correction of compromised model contributions. The architecture enables real-time monitoring through local DTs, secure model aggregation via FL, and targeted rollback using gradient subtraction, re-aggregation, or constrained retraining. A comprehensive simulation environment is developed to assess the impact of sensor drift, label noise, and adversarial updates across a federated fleet of aircraft. The experimental results demonstrate that FU methods restore up to 95% of model accuracy degraded by data corruption, significantly reducing false negative rates in early fault detection. The proposed system further supports auditability through cryptographic logging, aligning with aviation regulatory standards. This study establishes federated unlearning as a critical enabler for resilient, correctable, and trustworthy AI in next-generation AHM systems. Full article

This paper reviews the variability of passenger service time (PST) at the platform–train interface (PTI), a critical performance indicator in metro systems shaped by the infrastructure design, affecting passenger behavior and accessibility. Despite its operational importance, PST remains underexplored in relation to crowd management strategies. This review synthesizes findings from empirical and experimental research to clarify the main factors influencing PST and their implications for platform-level interventions. Key contributors to PST variability include door width, gap dimensions, crowd density, and user characteristics such as mobility impairments. Design elements—such as platform edge doors, yellow safety lines, and vertical handrails—affect flow efficiency and spatial dynamics during boarding and alighting. Advanced tracking and simulation tools (e.g., PeTrack and YOLO-based systems) are identified as essential for evaluating pedestrian behavior and supporting Level of Service (LOS) analysis. To complement traditional LOS metrics, the paper introduces Level of Interaction (LOI) and a multidimensional LOS framework that captures spatial conflicts and user interaction zones. Control strategies such as platform signage, seating arrangements, and visual cues are also reviewed, with experimental evidence showing that targeted design interventions can reduce PST by up to 35%. The review highlights a persistent gap between academic knowledge and practical implementation. It calls for greater integration of empirical evidence into policy, infrastructure standards, and operational contracts. Ultimately, it advocates for human-centered, data-informed approaches to PTI planning that enhance efficiency, inclusivity, and resilience in high-demand transit environments. Full article

In recent years, the biomedical startup industry has flourished, and yet, it still faces challenges in adapting to changing market demands. Meanwhile, the widespread use of single-use medical devices generates significant waste, posing threats to environmental sustainability. Addressing this issue has become a critical challenge for humanity today. The study aimed to delve into the specific difficulties faced by Taiwanese biomedical entrepreneurs during the innovation and development of medical devices from a sustainability perspective and to explore solutions. This study collected first-hand experiences and insights from Taiwanese biomedical entrepreneurs through a literature review and expert questionnaires. It employed Interpretive Structural Modeling to analyze the development stages and interrelationships of biomedical device startups for building sustainable biomedical innovation. The Clinical Needs Assessment is revealed as the most influential factor, shaping Regulatory Feasibility Evaluation, Clinical Trial Execution, and Market Access Compliance. Our findings provide a structured problem-solving framework to assist biomedical startups in overcoming challenges while incorporating energy-saving and carbon reduction processes to achieve environment sustainability goals. The results of this study show that biomedical innovation practitioners should prioritize integrating sustainability considerations directly into the earliest stage of a Clinical Needs Assessment. Full article

In wind-assisted orchard spraying operations, the dynamic response of leaves—manifested through changes in their posture—critically influences droplet deposition on both sides of the leaf surface and the penetration depth into the canopy. These factors are pivotal in determining spray coverage and the spatial distribution of pesticide efficacy. However, current research lacks comprehensive quantification and correlation analysis of the temporal response characteristics of leaves under wind disturbances. To address this gap, a systematic analytical framework was proposed, integrating real-time leaf segmentation and tracking, geometric feature quantification, and statistical correlation modeling. High-frame-rate videos of fluttering leaves were acquired under controlled wind conditions, and background segmentation was performed using principal component analysis (PCA) followed by clustering in the reduced feature space. A fine-tuned Segment Anything Model 2 (SAM2-FT) was employed to extract dynamic leaf masks and enable frame-by-frame tracking. Based on the extracted masks, time series of leaf area and inclination angle were constructed. Subsequently, regression analysis, cross-correlation functions, and Granger causality tests were applied to investigate cooperative responses and potential driving relationships among leaves. Results showed that the SAM2-FT model significantly outperformed the YOLO series in segmentation accuracy, achieving a precision of 98.7% and recall of 97.48%. Leaf area exhibited strong linear coupling and directional causality, while angular responses showed weaker correlations but demonstrated localized synchronization. This study offers a methodological foundation for quantifying temporal dynamics in wind–leaf systems and provides theoretical insights for the adaptive control and optimization of intelligent spraying strategies. Full article

This study investigates the critical link between child care accessibility and local labor force participation, addressing a gap in current research that often lacks local spatial granularity. While over half of the U.S. population resides in child care deserts, disproportionately affecting rural, low-income, and minority communities, the economic implications for local labor markets remain underexplored. Leveraging Michigan child care license data and Census tract-level demographic and employment characteristics, this research employs a spatial econometric approach to estimate the impact of geographic distance to child care facilities on labor supply using descriptive data. Our findings consistently demonstrate that increased distance to child care is significantly associated with reduced labor force participation. While female labor force participation is lower in areas with constrained access to child care, we also found that households with two parents are also less likely to have full labor force participation when access to child care is constrained. The cost-effective framework used here can be replicated to identify specific communities most impacted by child care-related employment disruptions. The analytical findings can be instrumental in targeting and prioritizing child care policy interventions. Full article

Accurate rainfall quantile estimation is critical for urban flood management, particularly given the escalating climate change impacts. This study comprehensively compared at-site frequency analysis and regional frequency analysis for sub-hourly rainfall quantile estimation, using data from 27 sites across Seoul. The analysis focused on Seoul’s disaster prevention framework (30-year and 100-year return periods). Employing L-moment statistics and Monte Carlo simulations, the rainfall quantiles were estimated, the methodological performance was evaluated, and Seoul’s current disaster prevention standards were assessed. The analysis revealed significant spatio-temporal variability in Seoul’s precipitation, causing considerable uncertainty in individual site estimates. A performance evaluation, including the relative root mean square error and confidence interval, consistently showed regional frequency analysis superiority over at-site frequency analysis. While at-site frequency analysis demonstrated better performance only for short return periods (e.g., 2 years), regional frequency analysis exhibited a substantially lower relative root mean square error and significantly narrower confidence intervals for larger return periods (e.g., 10, 30, 100 years). This methodology reduced the average 95% confidence interval width by a factor of approximately 2.7 (26.98 mm versus 73.99 mm). This enhanced reliability stems from the information-pooling capabilities of regional frequency analysis, mitigating uncertainties due to limited record lengths and localized variabilities. Critically, regionally derived 100-year rainfall estimates consistently exceeded Seoul’s 100 mm disaster prevention threshold across most areas, suggesting that the current infrastructure may be substantially under-designed. The use of minute-scale data underscored its necessity for urban hydrological modeling, highlighting the inadequacy of conventional daily rainfall analyses. Full article

This paper proposes an AI-driven automated test generation framework for vehicle control units (VCUs), integrating natural language processing (NLP) and dynamic variable binding. To address the critical limitation of traditional AI-generated test cases lacking executable variables, the framework establishes a closed-loop transformation from requirements to executable code through a five-layer architecture: (1) structured parsing of PDF requirements using domain-adaptive prompt engineering; (2) construction of a multidimensional variable knowledge graph; (3) semantic atomic decomposition of requirements and logic expression generation; (4) dynamic visualization of cause–effect graphs; (5) path-sensitization-driven optimization of test sequences. Validated on VCU software from a leading OEM, the method achieves 97.3% variable matching accuracy and 100% test case executability, reducing invalid cases by 63% compared to conventional NLP approaches. This framework provides an explainable and traceable automated solution for intelligent vehicle software validation, significantly enhancing efficiency and reliability in automotive testing. Full article

Background/Objectives: Clinical Research Nurses (CRNs) have emerged as pivotal actors in the conduct, coordination, and oversight of clinical trials globally. Over the past three decades, the role of the CRN has evolved in response to the increasing complexity of research protocols, ethical standards, and regulatory frameworks. Originating as task-oriented support figures, CRNs have progressively assumed broader responsibilities that include patient advocacy, protocol integrity, ethical vigilance, and interprofessional coordination. By mapping the global literature on CRNs, this review will examine how their role has been defined, implemented, and evaluated over the past three decades. Methods: A scoping review was conducted using JBI methodology and PRISMA-ScR guidelines. The search covered the peer-reviewed and gray literature from 1990 to 2024 across major databases. Data analysis combined traditional extraction with topic modeling, Multiple Correspondence Analysis, and k-means clustering to identify key themes. Results: From the 128 included studies, four major themes emerged: clinical trial management, role perception and team integration, professional competencies and development, and systemic barriers. Despite formal competency frameworks, CRNs face inconsistencies in role recognition, unstable contracts, and limited career pathways. Emotional strain and professional isolation are recurrent. Over time, their functions have evolved from task execution to broader responsibilities, including advocacy and ethical oversight. However, no studies reported patient-level outcomes, revealing a critical gap in the evidence base. Conclusions: CRNs play a vital but undervalued role in clinical research. Persistent structural challenges hinder their development and visibility. Enhancing institutional support and generating outcome-based evidence are necessary steps toward fully integrating CRNs into research infrastructures. Full article

This study presents a data-driven, multi-objective optimization framework for user-centric product form design, integrating affective response modeling with coupled constraint satisfaction. Initially, morphological analysis and aesthetic evaluation are employed to extract critical design elements, while cluster analysis segments users based on preference data. Dominance-based rough set theory is then applied to derive group-specific affective patterns, which are subsequently modeled using Genetic Algorithm-optimized Backpropagation Neural Networks (GA-BPNN). The framework leverages Non-dominated Sorting Genetic Algorithm II (NSGA-II) to generate Pareto-optimal solutions, balancing aesthetic preferences and engineering constraints across user groups. A case study on SUV form design validates the proposed methodology, demonstrating its efficacy in delivering optimal, user-group-targeted design solutions while accommodating individual variability and constraint interdependencies. The results highlight the framework’s potential as a generalizable approach for emotion-aware, constraint-compliant product design. Full article

Ensuring the structural integrity of reinforced concrete (RC) components in nuclear facilities exposed to extreme conditions is essential for safe decommissioning. This study investigates the impact of high-temperature exposure on RC pedestal structures supported by steel ring-shaped shear connectors—critical elements for maintaining vertical and lateral load paths in containment systems. Scaled-down cyclic loading tests were performed on pedestal specimens with and without prior thermal exposure, simulating post-accident conditions observed at a damaged nuclear power plant. Experimental results show that thermal degradation significantly reduces lateral stiffness, with failure mechanisms concentrating at the interface between the concrete and the embedded steel skirt. Complementary finite element analyses, incorporating temperature-dependent material degradation, highlight the crucial role of load redistribution to steel components when concrete strength is compromised. Parametric studies reveal that while geometric variations in the inner skirt have limited influence, thermal history is the dominant factor affecting vertical capacity. Notably, even with substantial section loss in the concrete, the steel inner skirt maintained considerable load-bearing capacity. This study establishes a validated analytical framework for assessing structural performance under extreme conditions, offering critical insights for risk evaluation and retrofit strategies in the context of nuclear facility decommissioning. Full article

This paper presents a practical framework for the design and real-time implementation of a Power Management System (PMS) for microgrids based on Supervisory Control Theory (SCT) for discrete-event systems. A detailed step-by-step methodology is provided, which covers the entire process from defining discrete events, modeling microgrid components, synthesizing supervisory controllers, and realizing them in MATLAB (R2024b) Stateflow. This methodology is applied to a case study, where a decentralized supervisor controller is designed for a microgrid containing a Battery Energy Storage System (BESS), a generator set (Genset), a wind and a solar generation system, critical loads, and noncritical loads. Unlike previous works based on SCT, the proposed PMS addresses the following functionalities: $\left(i\right)$ grid-connected and islanded operation; $\left(ii\right)$ peak shaving; $\left(iii\right)$ voltage support; $\left(iv\right)$ load shedding. Finally, a CHIL testing is employed to validate the synthesized SCT-based PMS. Full article

Background: Sodium–glucose cotransporter 2 (SGLT2) inhibitors have transformed type 2 diabetes mellitus (T2DM) management by promoting glucosuria, lowering glycated hemoglobin (HbA1c), blood pressure, and weight; however, their use is limited by genitourinary infections and ketoacidosis. Phytocannabinoids—bioactive compounds from Cannabis sativa—exhibit multi-target pharmacology, including interactions with cannabinoid receptors, Peroxisome Proliferator-Activated Receptors (PPARs), Transient Receptor Potential (TRP) channels, and potentially SGLT2. Objective: To evaluate the potential of phytocannabinoids as novel modulators of renal glucose reabsorption via SGLT2 and to compare their efficacy, safety, and pharmacological profiles with synthetic SGLT2 inhibitors. Methods: We performed a narrative review encompassing the following: (1) the molecular and physiological roles of SGLT2; (2) chemical classification, natural sources, and pharmacokinetics/pharmacodynamics of major phytocannabinoids (Δ⁹-Tetrahydrocannabinol or Δ⁹-THC, Cannabidiol or CBD, Cannabigerol or CBG, Cannabichromene or CBC, Tetrahydrocannabivarin or THCV, and β-caryophyllene); (3) in silico docking and drug-likeness assessments; (4) in vitro assays of receptor binding, TRP channel modulation, and glucose transport; (5) in vivo rodent models evaluating glycemic control, weight change, and organ protection; (6) pilot clinical studies of THCV and case reports of CBD/BCP; (7) comparative analysis with established synthetic inhibitors. Results: In silico studies identify high-affinity binding of several phytocannabinoids within the SGLT2 substrate pocket. In vitro, CBG and THCV modulate SGLT2-related pathways indirectly via TRP channels and CB receptors; direct IC₅₀ values for SGLT2 remain to be determined. In vivo, THCV and CBD demonstrate glucose-lowering, insulin-sensitizing, weight-reducing, anti-inflammatory, and organ-protective effects. Pilot clinical data (n = 62) show that THCV decreases fasting glucose, enhances β-cell function, and lacks psychoactive side effects. Compared to synthetic inhibitors, phytocannabinoids offer pleiotropic benefits but face challenges of low oral bioavailability, polypharmacology, inter-individual variability, and limited large-scale trials. Discussion: While preclinical and early clinical data highlight phytocannabinoids’ potential in SGLT2 modulation and broader metabolic improvement, their translation is impeded by significant challenges. These include low oral bioavailability, inconsistent pharmacokinetic profiles, and the absence of standardized formulations, necessitating advanced delivery system development. Furthermore, the inherent polypharmacology of these compounds, while beneficial, demands comprehensive safety assessments for potential off-target effects and drug interactions. The scarcity of large-scale, well-controlled clinical trials and the need for clear regulatory frameworks remain critical hurdles. Addressing these aspects is paramount to fully realize the therapeutic utility of phytocannabinoids as a comprehensive approach to T2DM management. Conclusion: Phytocannabinoids represent promising multi-target agents for T2DM through potential SGLT2 modulation and complementary metabolic effects. Future work should focus on pharmacokinetic optimization, precise quantification of SGLT2 inhibition, and robust clinical trials to establish efficacy and safety profiles relative to synthetic inhibitors. Full article

The integration of heterogeneous spatio-temporal datasets presents a critical challenge in geospatial data science, particularly when combining large-scale, passively collected “big” data with precise but sparse “small” data. In this study, we propose a novel framework—Multi-Channel Spatio-Temporal Data Fusion (MCST-DF)—that leverages transformer-based deep learning to fuse these data sources for accurate network flow estimation. Our approach introduces a Residual Spatio-Temporal Transformer Network (RSTTNet), equipped with a layered attention mechanism and multi-scale embedding architecture to capture both local and global dependencies across space and time. We evaluate the framework using real-world mobile sensing and loop detector data from the London road network, demonstrating over 89% prediction accuracy and outperforming several benchmark deep learning models. This work provides a generalisable solution for spatio-temporal fusion of diverse geospatial data sources and has direct relevance to smart mobility, urban infrastructure monitoring, and the development of spatially informed AI systems. Full article

Mathematical modeling plays a crucial role in supporting decision-making across a wide range of scientific disciplines. These models often involve multiple parameters, the estimation of which is critical to assessing their reliability and predictive power. Recent advancements in artificial intelligence have made it possible to efficiently estimate such parameters with high accuracy. In this study, we focus on modeling the dynamics of cryptocurrency market shares by employing a Lotka–Volterra system. We introduce a methodology based on a deep neural network (DNN) to estimate the parameters of the Lotka–Volterra model, which are subsequently used to numerically solve the system using a fourth-order Runge–Kutta method. The proposed approach, when applied to real-world market share data for Bitcoin, Ethereum, and alternative cryptocurrencies, demonstrates excellent alignment with empirical observations. Our method achieves RMSEs of 0.0687 (BTC), 0.0268 (ETH), and 0.0558 (ALTs)—an over 50% reduction in error relative to ARIMA(2,1,2) and over 25% relative to a standard NN–ODE model—thereby underscoring its effectiveness for cryptocurrency-market forecasting. The entire framework, including neural network training and Runge–Kutta integration, was implemented in MATLAB R2024a (version 24.1). Full article

Flood risk governance has gained increasing attention as climate change and urbanization amplify flood risks. While much of the literature has focused on national and supranational governance frameworks, sectoral integration, and public participation, there remains a critical gap in understanding horizontal coordination within municipal administrations—particularly in medium-sized cities. This study examines how local governments coordinate flood risk management across different departments and administrative units, identifying key challenges and enabling factors. Using a case study of Mörfelden-Walldorf, a medium-sized city in southern Hesse, Germany, we analyze the internal governance dynamics shaping flood resilience. The research highlights institutional fragmentation, sectoral silos, and resource constraints as key barriers to effective coordination while also identifying mechanisms that facilitate cross-departmental collaboration. By integrating insights from the public administration literature with flood governance scholarship, this study contributes to a more nuanced understanding of local-level flood risk governance. The findings provide practical implications for enhancing municipal flood resilience through improved governance structures and coordination mechanisms. Full article