Search Results (109)

Background/Objectives: The blood urea nitrogen-to-serum albumin ratio (BAR) is a recently established index that has been associated with systemic inflammation. This study aimed to evaluate the association between BAR levels and histopathological findings in patients with biopsy-proven MASLD. Methods: A total of 425 biopsy-proven MASLD patients from the Turkish MASLD Biobank were included in the analysis. Histological assessments were performed according to the SAF/FLIP algorithm. The BAR was calculated as indicated. Results: The median age was 49 [19–71] and54% of the patients were male (n = 229). A total of 204 (48%) and 377 patients (88.7%) had significant fibrosis and metabolic dysfunction-associated steatohepatitis (MASH), respectively. BAR was significantly lower among patients with significant fibrosis [3.11 (0.21–9.41) vs. 3.41 (1.37–11.49), p = 0.002]. The same trend was observed among patients with MASH (3.2 [0.2–11.5] vs. 4.6 [1.9–8.7]), p < 0.001). Also, there is a decline beginning from F0 to F4 fibrosis (p = 0.014). In multivariate analysis, BAR remained an independent predictor after adjustment for confounders (OR 0.813, p = 0.020). BAR showed an AUROC of 0.586 (95% CI: 0.532–0.640) for predicting significant fibrosis. Conclusions: This is the first study to evaluate BAR levels in patients with biopsy-proven MASLD, demonstrating that BAR levels were inversely associated with the presence of significant fibrosis. These findings show an association between declining BAR levels and impaired liver health. However, the results should be interpreted cautiously owing to their suboptimal statistical performance. Full article

(1) Background: As one of the world’s four major nuts, walnuts are rich in nutritional value; however, concerns regarding their allergenicity are becoming increasingly prominent. (2) Scope and Approach: This article provides a systematic review of the nutritional value and allergenicity of walnuts, the composition of major allergenic proteins, and their detection techniques. A particular focus is placed on elucidating the mechanisms by which different processing methods—including heat treatment, ultra-high pressure, ultrasound, low-temperature plasma, enzymatic treatment, and polyphenol modification—affect the structure and allergenicity of walnut allergenic proteins. (3) Key Findings and Conclusions: Current evidence suggests that processing techniques can alter the secondary and tertiary structures of walnut proteins, change the accessibility of linear or conformational epitopes, and reduce their Immunoglobulin E/Immunoglobulin G (IgE/IgG) binding capacity under certain in vitro conditions. Among these, high-temperature and high-pressure treatment, enzymatic hydrolysis, polyphenol modification, and combined processing strategies demonstrate promising potential for reducing walnut protein immunoreactivity. However, structural modifications, reduced antibody-binding capacity, or increased digestibility should not be directly interpreted as definitive evidence of reduced clinical sensitization. This paper summarizes the current status of the development and application of hypoallergenic foods, analyzes the technical challenges and future development directions, and aims to provide a theoretical basis and technical reference for the development of allergenicity-reduced walnut products. Full article

Renewable energy is being increasingly integrated into power grids. As a result, the three-phase grid-connected inverter (GCI) faces power transfer limitations caused by small-signal stability issues. To improve energy utilization and enhance stability, this paper employs an impedance-based method to analyze the small-signal stability power boundary of the GCI. This boundary is then quantified using the generalized Nyquist criterion (GNC). Our analysis reveals that the power boundary decreases as the grid short-circuit ratio (SCR) decreases or the phase-locked loop (PLL) bandwidth increases. To address this problem, we propose an impedance reshaping method that cancels the negative resistance effect introduced by PLL feedforward. This approach raises the small-signal stability power limit to the rated power and ensures stable operation under grid impedance variations and high PLL bandwidth. Finally, impedance analysis and experimental verification confirm both the theoretical correctness and the practical effectiveness of the proposed method in extending the stability power boundary. Full article

Lunar lava tubes are subsurface cavities generated by volcanic activity and are regarded as promising targets for exploration because they can offer natural shielding and potentially support future lunar infrastructures as protected shelters and scientific laboratories. Autonomous navigation in these environments remains challenging due to the absence of illumination, sparse or repetitive geometric features, uneven terrain, and intermittent communications that limit teleoperation. In this framework, the Italian Space Agency (ASI) is pursuing a dedicated mission, and OHB Italia has been appointed the prime contractor to perform a candidate system-architecture study for lava tube exploration. This paper presents the activities and results related to the definition of the subsurface Guidance, Navigation, and Control (GNC) algorithm for a rover/hopper system. To address the above constraints, this study investigates the requirements for autonomous onboard navigation, focusing on sensor selection for Simultaneous Localization and Mapping (SLAM) as a fundamental prerequisite for mission success. A weighted-criteria evaluation framework is developed to assess various sensing modalities, considering mission-specific constraints. Based on this analysis, a sensor configuration optimized for GPS-denied and unilluminated environments is proposed. The effectiveness of the selected sensing architecture is validated through a simulation campaign conducted in simulation environments (CoppeliaSim v4.10.0/MATLAB 2025a), using two representative SLAM pipelines (ICP and LOAM) in LiDAR-only and LiDAR + IMU configurations. Finally, a modular Guidance, Navigation, and Control (GNC) architecture incorporating frontier-based exploration is proposed. Full article

Artificial Intelligence (AI) is emerging as a transformative enabler in aviation, with applications spanning Guidance, Navigation and Control (GNC), Air Traffic Management (ATM), and predictive maintenance. However, the adoption of AI in safety-critical domains remains constrained by the absence of established certification guidance. Traditional standards such as Aerospace Recommended Practice (ARP), ARP4754B, ARP4761A, DO-178C, and DO-254 assume deterministic behaviour and verifiable logic, whereas AI exhibits adaptive and non-deterministic characteristics. Regulatory initiatives, including the European Union Artificial Intelligence Act, the European Union Aviation Safety Agency (EASA) AI Roadmap 2.0, the Federal Aviation Administration (FAA) AI Safety Assurance Roadmap, and ISO/IEC Technical Report (TR) 5469:2024, signal progress but remain fragmented, exploratory, and often limited to low-level autonomous use cases. This study adopts a qualitative approach combining literature and standards analysis with expert interviews to identify gaps in post-deployment assurance, data governance, explainability, and accountability. A conceptual life cycle-oriented framework is proposed that embeds AI-specific assurance activities such as dataset validation, iterative verification, drift detection, and retraining oversight into established certification processes. The framework extends classical and emerging verification and validation models into operational service, linking machine learning constituents to system-level safety arguments and regulatory expectations to support the development of trustworthy and certifiable AI-enabled aviation systems. Full article

The design of metal nanoparticle-on-a-mirror (NPOM) provides a powerful strategy for optical enhancement in gap plasmonics. Here, we report a systematic numerical study on an NPOM structure composed of gold nanocubes (GNC) and a continuous gold film via the finite element method (FEM). First, we simulated the near-electric field distribution of isolated GNC in a homogeneous medium and compared it with that of the GNC-based NPOM structure, revealing the dominant role of plasmon coupling in the gap region. Second, we systematically investigated the influence of the thickness of the dielectric layer between the GNC and the gold film on the optical enhancement characteristics in the gap region. The results show that the maximum electric field intensity of the resonance peak decays rapidly when the thickness of the dielectric layer is less than 2 nm, decreasing from 5048 (t = 0.5 nm) to 1032 (t =2 nm). Third, we further investigated the influence of the polarization angle of the incident light on the optical enhancement in the gap region. Finally, the dielectric environment n₀ and the refractive index n of the dielectric layer were studied. This work elucidates the unique gap plasmon coupling mechanisms of GNC-based NPOM structures and provides a precise tuning strategy for key structural and optical parameters, endowing the structure with important application prospects in sensing, energy conversion, and photodetection. Full article

Feature-based visual–inertial odometry (VIO) often suffers from initialization failures and tracking drift under degraded visual conditions, such as low-texture regions, abrupt illumination changes, and scenes with a high ratio of dynamic correspondences. We present RISE-VIO, a real-time inertial-navigation-system-centric (INS-centric) visual–inertial odometry system that improves robustness by introducing GNC-style robustification into two failure-critical stages: initialization and per-frame pose estimation. For robust initialization, we develop a GNC-based decoupled rotation–translation initialization module with a two-stage observability gate, consisting of (i) rotation-compensated parallax-rate screening and (ii) a spectral-stability test on the linear global translation (LiGT) system. For online robustness, we design an IMU-prior-guided GNC-EPnP module to selectively downweight or reject outlier correspondences during pose estimation. Experiments on public benchmark datasets show that RISE-VIO achieves more reliable initialization and more stable trajectory estimation in challenging visual conditions while maintaining real-time performance. Additional Monte Carlo perspective-n-point (PnP) evaluations further support the robustness of the proposed pose estimation module under severe outlier contamination. Full article

Unmanned Aerial Systems (UAS) offer sustainable solutions for urban last-mile logistics, yet existing navigation algorithms struggle with the complexity of dynamic metropolitan environments. This study optimises a reinforcement learning (RL)-based guidance, navigation, and control (GNC) algorithm using a Proximal Policy Optimisation (PPO) model within a high-fidelity simulation of Bristol City Centre. The primary contribution is training the RL model to autonomously detect and avoid dynamic obstacles, specifically manned aircraft, to ensure safe and legal drone operations. Additionally, flight operations are continuously monitored via a Structured Query Language (SQL) database to verify compliance with low airspace regulations. Simulation results demonstrate that the proposed framework achieves high obstacle detection accuracy under nominal conditions, while the implementation of curriculum learning significantly enhances the system’s adaptability and recovery capabilities during high-speed, dynamic encounters. Full article

Multi-UAV collaborative mapping in communication-constrained indoor environments is often hampered by a trade-off between overall map refinement and the timely completion of safety-relevant shared regions. In high-density or unmapped areas, network congestion can delay the updates that matter most for close-proximity coordination, because standard bandwidth allocation does not distinguish between general map refinement and hotspot-related spatial data. To address this issue, we propose a resilient scheduling framework that prioritizes globally useful map updates while improving safety-relevant hotspot completeness under unreliable links. At its core is a Safety Reserve allocation strategy for “hotspot” submaps—areas where UAV trajectories overlap or approach unknown frontiers. By enforcing this reserve, the system directs a limited uplink budget to hotspot-related updates earlier during congestion. To remain useful under packet loss, we implement a prefix-decodable hierarchical data structure over a lightweight stateless protocol, allowing immediate fusion of valid partial updates. The framework identifies hotspots using feedback from a Lambda-Field risk model and a truncated least squares solver with graduated non-convexity (TLS–GNC) pose-graph optimizer. Experiments on S3DIS and ScanNet under partition-based two-agent emulation show that the proposed method improves hotspot-band completeness and progressive mapping quality over the tested baselines, especially under packet loss. Full article

A characterization study of two by-products from the cork stopper industry was conducted to assess their suitability as components of growing media (substrate). Granulate of natural cork stopper (GNCS) and granulate of technical cork stopper (GTCS) were studied, evaluating their chemical composition, fractionation, effects on physical and chemical properties, mineral elements, and phytotoxicity. The two by-products were granulometrically classified into four categories: very fine fractions (≤1 mm), fine fractions (>1 and ≤2 mm), intermediate fractions (>2 and ≤5 mm), and coarse fractions (>5 and ≤10 mm). The highest proportion of granulates was observed within the intermediate fraction (>2 and ≤5 mm). GTCS presented significant limitations regarding the assessed properties, while the very fine fractions (≤1 mm) were the most attractive in both granulates. Therefore, selecting raw materials and their fractionation are vital for predicting the performance of growing media and establishing their suitability for promoting plant growth and productivity. Thus, these two by-products of the cork stopper industry have desirable characteristics as components of growing media. Full article

As smart grids evolve into complex cyber-physical systems, conventional static defenses struggle to address time-varying topologies and Advanced Persistent Threats (APTs). We propose the Security Framework for Resilient Smart Grids based on Self-Organizing Graph Neural Cellular Automata (SG-GNC). Specifically, a Neural Homeostatic Embedding (NHE) mechanism utilizes variational graph autoencoders to construct a continuous health manifold for unsupervised anomaly detection, while a Neural Cellular Automata (NCA) engine employs shared-weight local rules to empower nodes with decentralized self-healing capabilities. Finally, a Generative Adversarial Immunity (GAI) strategy facilitates active defense co-evolution, enhancing robustness against zero-day attacks. Experimental results on the IEEE 118 and 300-bus systems demonstrate an average detection accuracy of 98.23%, significantly outperforming benchmarks. In scenarios involving dynamic topology and zero-day attacks, the framework maintains over 96% accuracy with an inference latency of only 9.45 ms. These findings validate the capability of SG-GNC to provide resilient, endogenous defense in complex heterogeneous environments. Full article

With the increasing complexity of the space electromagnetic environment, traditional omnidirectional antenna-aided communication and networking techniques can no longer meet the collaboration requirements of aircraft clusters. To achieve goals such as anti-jamming, anti-interception, and enhanced spatial multiplexing, an increasing number of aircraft are being equipped with high-gain directional antennas. However, modeling of directional antenna-constrained Flying Ad Hoc Networks (FANETs) is far more complex than modeling of omnidirectional antenna-aided networks. The former task is highly dependent on the real-time flight state and the spatial topology of the network. In response to the communication challenges posed by directional networking of highly-dynamic aircraft clusters, this study proposes an antenna selection and beam pointing control algorithm, which is deeply integrated with the aircraft’s Guidance, Navigation, and Control (GNC) system. By introducing parameters that characterize dynamic flight state, such as position and attitude information, and combining them with high-precision multi-coordinate system transformations and spatial geometric analysis methods, the proposed algorithm enables the real-time optimization of antenna selection and beam pointing under the relative motion trends of aircraft. It effectively maintains high-quality connections between flying nodes. Digital simulation and physical experiment results demonstrate that the proposed method can accurately calculate the appropriate antenna selection and determine precise beam pointing directions based on the position data of flying nodes. This provides an important reference for the design of optimized communication strategies used in directional networking of highly-dynamic aircraft clusters. Full article

The component-level line of sight (LOS) angle measurement of spacecraft is much desired during space rendezvous, especially for component-related operations, such as component status evaluation, component repair, etc. However, most existing methods rarely consider the component approaching scenario where a continuous, stable, real-time LOS angle measurement method for the component of interest is needed. In this paper, a continuous robust component-level LOS angle measurement method with high computational efficiency applicable to the approach of the key component is proposed. Firstly, an adaptive gamma correction method is introduced to enhance the image quality in complex and variable lighting environments. Secondly, optimized thresholding that exploits information entropy is proposed to identify the pixels that are supposed to be the target from the background. Region detection is subsequently performed to segment the target region into suspected component regions, which can account for target changes during the approach by seamless parameter adaptation. Then, solar panels are recognized and accurately segmented based on the prior knowledge of their spatial relationship with other components and unique shape features. Finally, the centers of solar panels are localized and their LOS angles are calculated. Extensive experiments are conducted to demonstrate the performance of our proposed method, including the verification of the superiority of the solar panel recognition and segmentation method using both simulated images generated by an image simulator and actual images taken by a camera in a dark-room considering the actual lighting in space, and the validation of the ability of supporting real-time component-level LOS angle measurement by ground semi-physical experiments with a guidance, navigation and control (GNC) system incorporated to simulate an on-line dynamic approach. Full article

This paper details the design of a guidance architecture, in the form of a layered, finite state machine, meant to enable safe and autonomous rendezvous operations. The onboard software uses relative state parametrization based on relative orbital elements which provide significant geometrical insight into the shape of the relative orbit. The development is structured in two main steps: first, novel closed-form impulsive control schemes, derived from the Gauss Variational Equations expressed in a velocity-aligned frame, are formulated. These complement available strategies from the literature and generalize them for arbitrarily eccentric reference orbits. Secondly, the definition of the guidance layer provides the chaser spacecraft with the capability to select, schedule, and execute the proper maneuvers to complete a given rendezvous scenario, ensuring operational safety and predictability. The functionality and performance of the implemented architecture are analyzed through numerical tests in a linear propagator and a high-fidelity non-linear simulator. The results provide validation of the developed maneuvers’ strategies, as well as demonstrating how the proposed guidance architecture can be used in a straightforward fashion across different target orbit scenarios, while guaranteeing the same level of passive safety. Full article

Sub-synchronous oscillations (SSOs) induced by the interaction between doubly fed induction generators (DFIGs) and weak grids pose a critical threat to the grid-connected stability of DFIG-based wind power systems. In this paper, a dual-damping-term compensation filter based on the concept of motion-induced amplification (MIA), together with an optimized design method using a linear quadratic regulator (LQR), is applied to the DFIG system. The effectiveness of the proposed approach in suppressing DFIG shafting oscillations and mitigating grid-connected frequency coupling is verified, and the underlying mechanisms are thoroughly investigated. By establishing a shafting dynamics model for the DFIG and a frequency-coupled oscillation impedance model, this study focuses on revealing the differentiated impacts of the dual damping parameters (${\mathrm{Z}}_{\mathrm{p}}$ and ${\mathrm{Z}}_{\mathrm{q}}$) on system stability under two operating modes: maximum power point tracking (MPPT) and constant power operation. Stability analysis based on the generalized Nyquist criterion (GNC), together with time-domain simulations, demonstrates that coordinated optimization of the dual damping terms can effectively suppress shafting oscillations and frequency coupling, thereby significantly enhancing the grid-connected stability of DFIG systems. Full article